Abstract

     Stuttering is a neurodevelopmental disorder affecting 5–8 % of preschool-age children, continuing into adulthood in 1 % of the population. The neural mechanisms underlying persistence and recovery from stuttering remain unclear and little information exists on neurodevelopmental anomalies in children who stutter (CWS) during preschool age, when stuttering symptoms typically first emerge. Here we present findings from the largest longitudinal study of childhood stuttering to date, comparing children with persistent stuttering (pCWS) and those who later recovered from stuttering (rCWS) with age-matched fluent peers, to examine the developmental trajectories of both gray matter volume (GMV) and white matter volume (WMV) using voxel-based morphometry. A total of 470 MRI scans were analyzed from 95 CWS (72 pCWS and 23 rCWS) and 95 fluent peers between 3 and 12 years of age. We examined overall group and group by age interactions in GMV and WMV in preschool age (3–5 years old) and school age (6–12 years old) CWS and controls, controlling for sex, IQ, intracranial volume, and socioeconomic status. The results provide broad support for a possible basal ganglia-thalamocortical (BGTC) network deficit starting in the earliest phases of the disorder and point to normalization or compensation of earlier occurring structural changes associated with stuttering recovery.

Introduction

     Developmental stuttering is a complex neurodevelopmental disorder (Smith and Weber, 2017) that disrupts the fluent flow of speech production. It is characterized by frequently occurring involuntary repetitions and prolongations in speech sounds, in addition to prolonged articulatory posture, and/or avoidance and struggle behaviors (Van Riper, 1971). Stuttering affects 5–8 % of preschool-age children (Yairi and Ambrose, 2013) and remains as a chronic speech disorder in 1 % of the general population. Typical onset of stuttering is reported to occur at 30–48 months (Bloodstein and Ratner, 2008, Reilly et al., 2013, Yairi and Ambrose, 2005) with approximately 80 % of children naturally recovering 24–36 months after the onset of stuttering (Yairi and Ambrose, 2013, Yairi and Ambrose, 2005, Yairi and Ambrose, 1999). Around this same developmental period, the neural systems supporting executive function, language, and speech-motor control undergo rapid and vigorous development (Smith and Weber, 2017, Almli et al., 2007, Chang et al., 2019, Friederici, 2006, Gilmore et al., 2018).

     In the past 20 years, an increasing number of neuroimaging studies have been conducted to understand the possible neural bases of stuttering. Convergent findings from systematic reviews and meta-analyses have highlighted several neuroanatomical differences in children and adults who stutter (AWS) compared to fluent speakers (Smith and Weber, 2017, Chang et al., 2019, Belyk et al., 2015, Budde, 2014, Ingham et al., 2005, Neef et al., 2015). Structural and functional anomalies in the left speech motor neural system, including the inferior frontal gyrus (IFG), posterior superior temporal gyrus (STG), basal ganglia-thalamo-cortical (BGTC) loop and the cerebellum have been associated with stuttering (see Chang et al., 2019 for review). In addition to gray matter anomalies, children who stutter (CWS) were found to have less white matter volume (WMV) bilaterally in the forceps minor of the corpus callosum (Beal et al., 2013) while another study found no significant differences (Choo et al., 2012).

Conclusion

     Regional gray and white matter volume differences across the whole brain were examined in preschool- and school-age CWS (both persistent and recovered) compared to age matched peers in the largest MRI dataset collected for this clinical population to date. Longitudinally collected scans that spanned up to 4 years per participant allowed tracking of developmental trajectories that differentiated among the groups. The results provide broad support for a possible BGTC network deficit starting at the early phase (preschool-age) of the disorder, involving the putamen, nucleus accumbens, left IFG/vPMC, and corticostriatal tracts. The deficits in input regions of the BGTC network appear to affect the output regions of the network including the thalamus in later phases of stuttering (school-age). The current data also provide insights into neural bases of natural recovery from stuttering during childhood. Children who recover from stuttering showed increased WMV in motor projection fibers, left AF, corpus callosum, and cerebellar peduncle around the dentate nuclei, suggesting that normalization or successful compensation of stuttering-linked neural deficits. Similar volume increases in corpus callosum, and cerebellar peduncle were also observed in pCWS, indicating an incomplete compensation. These results provide substantial new insights into possible neural bases of stuttering onset, persistence, and recovery during childhood.

Abstract

     The aim of this paper is to develop an algorithm to enhance speech recognition of a stuttered speech. Stuttering is a disorder that affects the fluency of speech by involuntary repetition, prolongation of words/syllables, or involuntary silent intervals. Current speech recognition systems fail to recognize stuttered speech. Methods to detect stutter have been reported in literature but efficient techniques for stutter correction have not been reported. This paper addresses this issue and proposes methods to detect and correct stutter within acceptable time limits. To remove prolongation(s) from the sample, amplitude thresholding through neural networks is developed. Repetitions are removed through string repetition removal algorithm using an existing Text-to-Speech (TTS) system. Thus, the output signal, void of all stutters, produces better speech recognition.

Introduction

     Stuttering is a speech disorder characterized by repetition of sounds, syllables, or words; prolongation of sounds. An individual who faces this disorder knows what he or she intend to say but is unable to produce fluent speech. Millions of people, in today’s world, suffer from various speech disorders like stuttering, lisp, and articulation disorder. This often renders them unable to utilize certain things that a normal person takes for granted, like speech recognition systems.

     Stuttering disorder is characterized by disruptions in the production of speech sounds, called disfluencies. Disfluencies are not necessarily a problem; however, they can hinder communication when a person produces too many of them. Most people often produce brief disfluencies. For instance, some words are repeated or prolonged while others are preceded by an ‘um’ or ‘uh’. In most cases, stuttering has an impact at least on some daily activities. The specific everyday activities that a person finds challenging to perform, vary across individuals. For example, for some people, communication difficulties happen only during specific activities, like talking on the phone, talking before large groups, utilizing everyday tools that use speech as inputs. An author claims, “Stuttering cannot be permanently cured; it may go into remission for a time, or clients can learn to shape their speech into fluent speech with the appropriate speech pathology treatment” [1].

     The main objective of this paper is to present an algorithm that efficiently detects and corrects stutter in a speech segment of a person with stuttering speech disability. The proposed algorithm gives an accuracy level of 86% for 50 stutter speech samples. Two algorithms were used for more precise stutter removal system that can be built on any device.

     The developed system can be incorporated into any existing speech recognition system. It can also serve as a speech therapy system where a user suffering from stutter can sound like the correct output obtained from the system. Hence the device can be used by people suffering from stutter to use the existing virtual assistant services, or talk to others with confidence using the device. This would enhance the level of communication amongst people with this disorder.

Abstract

     Purpose: The aim of this study was to evaluate the effect of tele-speech therapy on the recovery of patients with a stutter.
Methods: This research was an interventional study conducted on the patients visiting two rehabilitation centres. In this study, patients with a stutter participated in the tele-speech therapy sessions via Skype. Stuttering Severity Instrument-Fourth Edition was used to determine the severity of stutter before and after the intervention.
Results: The results showed a significant difference in the mean scores of stuttering severity before and after tele-speech therapy (p ¼ 0.001). Our findings showed a mean reduction of 13.8% in stuttering scores. More than half of the patients (56.6%) reported a “high” and “very high” satisfaction with this treatment method.
Conclusions: Tele-speech therapy is an effective way for the treatment of stuttering patients irrespective of their age, gender, and educational background. This method can improve patient’s speech and reduce stuttering.

Introduction

     Today, telemedicine is profoundly used as a means for the remote interaction of patients with healthcare providers with the goal of providing better access to the providers, reducing medical costs, preventing unnecessary patients referrals to healthcare centres, reducing waiting time for appointments and improving patient care [1]. One of the important telemedicine subspecialties is telerehabilitation, which provides services to disabled patients by utilising telecommunication technologies; and minimises distance, time and cost barriers for delivery of care [2].

     Early treatment of speech and language disorders increases the chance of improving communication skills in patients. According to the Scientific Secretary of the 12th Conference of Speech Therapy, 5–6% of the Iranian population suffer from speech disorders, among which stutter with 0.7 prevalence is a common disorder [3]. Access to appropriate treatment is not provided to all these patients due to unavailability and lack of providers such as speech-language pathologists in some regions, the high cost of transferring patients, patients travel fatigue, and long rehabilitation process [4]. Tele-speech therapy can be an effective alternative when a speech therapist is not available [5]. In Iran, different languages with different dialects and accents are spoken. In the field of speech therapy, in order to provide optimal service, the full conformity of the language and accent of therapist and patient is essential. For example, an Azeri-language patient needs a therapist who speaks the same language but this therapist may not be available in the patient’s region [6]. The use of technology as a facilitator provides the possibility of communication with a speech therapist [7] and offers fair access to this type of service for all patients [4]. Although access to the technology in Iran is not comparable to most of the countries across the globe, a previous study in Iran showed that the applied infrastructure for the treatment of stutter is appropriate [8]. In this study, the participants sometimes experienced a delay in displaying videos due to a low Internet connection, however, repeated use of the infrastructure during the treatment led to more satisfaction [8].

Conclusions

     Tele-speech therapy effectively improves the speech of patients with stuttering and reduces the stuttering severity of patients. Beside the effectiveness of this therapeutic method, the majority of patients were satisfied with this method. Due to the uneven distribution of speech therapists in many countries like Iran, the use of tele-speech therapy can improve patients’ access to effective treatment without the need to travel. It is recommended that health policymakers, the ministry of health authorities, budget allocation officials, and health system managers make the critical decisions and take serious actions to implement telespeech therapy for patients who have no face-to-face access to this service.

Abstract

     Stuttering speech recognition is a well-studied concept in speech signal processing. Classification of speech disorder is the main focus of this study. Classification of stuttered speech is becoming more important with the enhancement of machine learning and deep learning. In this study, some of the recent and most influencing stuttering speech recognition methods are reviewed with a discussion on different categories of stuttering. The stuttering speech recognition process is divided mainly into four segments-input speech pre-emphasis, segmentation, feature extraction, and stutter classification. All these segments are briefly elaborated and related researches are discussed. It is observed that different traditional machine learning and deep learning classification approaches are employed to recognize stuttered speech in last few decades. A comprehensive analysis is presented on different feature extraction and classification method with their efficiency.

Introduction

     Human speech is employed for communication to precise their feelings, ideas, and thoughts. A sort of speech problem where the flow of speech is interrupted is understood as stuttering or generally heard as stammering. It is a speech disorder where the sufferers want to say but have difficulty saying it. Stutterers feel same of difficulty while communicating with other people, which often affect a person’s quality of life and interpersonal relationships. It creates negative vibes influencing job performance and opportunities. A huge number of people i.e., more than 70 million people worldwide are affected by this problem. This number is about 1% of the total population [41]. It is observed whenever they communicate, receiver person feels irritated by hearing the prolonged words and most of the time don’t understand. E. Charles Healey, in his article, sought a discussion of children reaction to stuttering, impacts of stuttering with listener recall and comprehension of story information listeners’, interferes stuttering on listeners’ reactions and listeners’ reaction on strategies and therapy programs on stuttering [21]. An enormous source of evidence-based information about the cited things has been provided in the extant literature. Stuttering, aging processes and several neurological diseases in relation to speech can be identified by muscular stiffness and analyzing the latency times in verbal reactions, their coordination and their patterns of the muscles (respiratory, glottal, oromandibular) involved in speaking [50]. Being an interdisciplinary field of research among different domains like speech pathology, psychology, speech physiology, acoustics and signal analysis, the field of stuttering speech recognition is one area of interest for the researches over previous few decades. Traditionally, the assessment of stuttering is done by manually counting and classifying the occurrence of disturbances in stuttering speech. Time of disfluency in total speech is also considered as a measurement to assess stuttered speech. But this type of manually stuttering assessment varies depending on different speech language pathologist (SLP). So, it is time consuming and liable to error.

Conclusion and future work

     Speech is the communication carrier to express human thoughts, feelings and ideas. Stuttering, or stammering is a disorder of speech which affects millions of people in the glove. In the field of stuttered speech recognition, different machine learning models were applied for analysis and classification over the last few decades. In this study, different machine learning and deep learning models with their application in stuttered speech recognition are discussed. The 3 major classifiers i.e., ANNs, HMMs and SVM have been used to classify different types of stutterers. Deep learning algorithms have become very popular nowadays over traditional machine learning algorithms for stuttering speech recognition, discussed briefly in this study. The major challenges like small volume unlabeled data, similarity between different stuttering classes are observed. Moreover, an input speech file sometimes contains more than one types of stuttering which creates difficulties on labeling. Most of the research had been concentrated on prolongation and repetition types of stuttering. Some work on Interjection types of stuttering was also done but work on classification of broken words, revisions, incomplete phrases types of stuttering is almost nil. Most of the researchers labeled different no of stuttered speech from UClASS database manually in order to train their model. Different features like LPC, LPCC, PLP and MFCC were used in the previous researches to train and test the models among them MFCC features was extensively used. Reviews and comparisons of earlier researches have been highlighted in this paper. Accuracy in respect to recognition and correction of stuttering speech may be improved by employment of modified feature extraction algorithm and different deep learning based algorithms on large database.

Abstract

     Introduction: It is estimated that 8% of children who stutter (CWS) have autism spectrum disorder (ASD) Briley & Ellis (2018). There is evidence that interventions for CWS and interventions for children with ASD can be effective, but there is little evidence to guide clinical decision making when working with CWS with a co-existing diagnosis of ASD. Palin Parent–Child Interaction (PCI) therapy Kelman & Nicholas (2020) is an evidence-based intervention for CWS, with the authors suggesting that the approach may be beneficial for CWS with ASD. The aim of this study was to examine outcomes for three CWS with ASD who received Palin PCI at a specialist centre for stuttering in London. Method: The participants were three CWS with ASD aged 4;5, 6;7 and 7;7. Assessments were administered before therapy, and then at three, six and twelve months after therapy began. Outcome measures included stuttering frequency, child’s communication attitude, parents’ perception of the impact of stuttering on the child, the severity of stuttering and its impact on the parents, and parents’ knowledge and confidence in managing stuttering. Results: All three children showed improvement in three or more variables. Four out of five parents reported reduced impact of stuttering on the child and themselves following therapy, and change was maintained one year post-therapy. All five parents reported increased knowledge of stuttering and confidence in managing it after therapy, and four parents maintained these changes for a year. Conclusions: Over a one year period, these CWS with ASD who received Palin PCI showed change across multiple variables. The observed increases in parent knowledge and confidence were comparable to previously published data. These preliminary findings suggest that CWS with ASD and their parents can benefit from Palin PCI therapy and that further experimental evaluation of this approach with this client group is indicated.

Introduction

Stuttering and autism spectrum disorder

     Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterised by early onset difficulties in social communication and unusually restricted, repetitive behaviour and interests (American Psychiatric Association, 2013). It may present with or without intellectual disability, and with or without language impairment (Lai et al., 2014). Shriberg et al. (2001) first identified that stuttering and other forms of dysfluency may occur within adults with ASD. A strong genetic component is evident for both ASD and stuttering (Tick et al., 2016; Yairi & Ambrose, 2013). Subsequent studies have identified stuttering in preschoolers with ASD (Plexico et al., 2010), school-aged children with ASD (Scott et al., 2014; Sisskin, 2006; Sisskin & Wasilus, 2014), and adolescents with ASD (Sisskin, 2006).

Conclusions

     The findings indicate that CWS with ASD and their parents can benefit from Palin PCI. All three children showed improvement in three or more variables. Four out of five parents reported reduced impact of stuttering on the child and on themselves after therapy and change was maintained one year after therapy. All five parents reported increased knowledge of stuttering and confidence in managing it after therapy and change was maintained one year after therapy for four parents. The findings indicate that further investigations of Palin PCI therapy with CWS with ASD are merited, and outcome measurement tools need to be validated with this clinical subpopulation.

Abstract

     Introduction: Stuttering may have a holistic effect on the quality of life of a person who stutters by limiting participation in social situations, resulting in feelings of isolation and frustration, leading to difficulties in education and employment and increasing the likelihood of mental health problems. Even young children who stutter may have negative experiences of speaking. Therefore, it is important to treat stuttering behavior effectively in both children and adults. The purpose of this paper was to systematically review group and case studies about the effectiveness of behavioral stuttering interventions to provide evidence-based guidelines for clinicians. Methods: Systematic data retrieval was conducted in four electronic databases (PsycINFO, CINAHL, PubMed, Cochrane). The assessment of search results was conducted according to predetermined inclusion and exclusion criteria by two independent judges. The methodological quality of each paper was assessed using strict criteria to include only high-quality research. Results: The search revealed 2293 results, and 38 papers (systematic reviews N=3, group design studies N=21 and case studies N=14) with acceptable methodological quality were included. The data show that there is most evidence about the treatment of early childhood stuttering, very little evidence about school-aged children and some evidence about adults. The most convincing evidence is about the Lidcombe Program in the treatment of young children who stutter, but also other methods have promising evidence. Our data imply that in the treatment of adults who stutter, holistic treatments may influence speech fluency and overall experience of stuttering behavior. Speech restructuring treatments may have a positive effect on overt characteristics of stuttering, but not on covert stuttering behavior.

Introduction

     Stuttering is characterized by repetitions or prolongations of sounds, syllables, or words, or by hesitations or pauses that disrupt the rhythm of speech, according to the International Classification of Diseases 10 (ICD-10; World Health Organization, WHO, 2016). The forthcoming ICD-11 includes a more comprehensive description of stuttering (WHO, 2019), that resembles the definition in the Diagnostic and Statistical Manual of Mental disorders fifth edition (DSM-5). In DSM-5 the definition of a childhood-onset fluency disorder (315.35) also includes blocking and avoidance or substitution of words. Furthermore, it recognizes the effects of stuttering on social communication, personal, family, educational, occupational, or other important areas of functioning (American Psychiatric Association, 2013). Moreover, the International Classification of Functioning, Disability and Health (ICF) provides a more holistic description of stuttering compared to the ICD-10 and considers the structures and functions of the body, how individual and environmental factors affect stuttering and how these factors influence activity and participation (WHO, 2001; Yaruss & Quesal, 2004; Yaruss, 2007).

     Recent studies on the effects of stuttering have revealed the multifaceted nature of stuttering more deeply (see e.g., Gerlach et al., 2018; Iverach, Jones et al., 2009, 2009b, 2010; Iverach & Rapee, 2014). As early as at the age of three years, a child may have more negative experiences around speaking than fluent peers (Vanryckeghem et al., 2005) and in preschool stuttering has the potential to provoke negative peer responses (Langevin et al., 2009). Adolescents who stutter have reported being teased or bullied and having communication competence below average (Erickson & Block, 2013). Parents of children who stutter experience feelings of helplessness, uncertainty, fear, and difficulties managing their child’s frustration, have concerns about academic performance, and feel empathy for their child (Erickson & Block, 2013; Plexico & Burrus, 2012). Even before an overt dysfluency of speech occurs, a person who stutters (PWS) may feel stuck or helpless (Tichenor & Yaruss, 2018, 2019), and start to avoid speaking because of stuttering (Lowe et al., 2017). Thus, stuttering may limit a speaker’s ability to participate in conversations, result in feelings of loneliness and isolation, lead to problems in social interaction, create a sense of helplessness, and be accompanied by social anxiety (see e.g., Iverach et al., 2017; Manning & DiLollo, 2018, 188–189; Yaruss, 2007). For adults who stutter, communicative participation is predicted by self-esteem, self-efficacy, and social support in addition to the severity of speech disfluencies (Boyle et al., 2018). In fact, self-efficacy is a positive predictor of the quality of life for PWS, while a high frequency of stuttering, on the other hand, is a negative predictor of self-efficacy (Carter et al., 2017). Moreover, speech fluency and self-confidence have been reported to be inextricably related (Carter et al., 2019). Therefore, stuttering may have a profound negative emotional, behavioral, and cognitive impact on a speaker’s life regardless of the age of the PWS (Carter et al., 2017; Guttormsen et al., 2021; Tichenor & Yaruss, 2018, 2019; Werle et al., 2021; Yaruss, 2007).

Conclusions

     Our results indicate that there are several effective treatment options for young children who stutter. Most evidence is available about the LP, which solely targets reducing %SS. Other treatments targeting multidimensional aspects of stuttering have shown promising results, especially the RESTART-DCM. These results agree with an earlier review (Brignell et al., 2021). The data from the case studies also support Palin PCI therapy. For adults, there are several treatment options available. Speech restructuring treatments reduce stuttering, when measured with %SS. However, they do not reduce covert aspects of stuttering, such as social anxiety. Because of the different inclusion criteria than in earlier reviews or meta-analyses (Brignell et al., 2020; Connery et al., 2021), our data supports treatments that address the multifaceted nature of stuttering and include elements of both speech modification and methods targeting psychological and social effects of stuttering. There are not sufficient effectiveness studies available for school-aged children who stutter, so we need to rely on research about young children and adolescents/adults and perhaps combine elements of both based on the individual situation and the age of the child.

1- Introduction

2- Methods

3- Results

4- Discussion

References

Abstract

Objective: Stuttering is a developmental disorder of speech production with a dynamic and multifactorial nature. Scientific theories mentioned the role of auditory processing disorder in stuttering. Investigating the auditory processing in stuttering would provide insights into the mechanisms of stuttering. The details of basic auditory processing in children with stuttering (CWS) continue to remain uncertain. This study aimed to investigate the auditory temporal processing (ATP) in CWS and also its relationship with the stuttering severity. Methods: The participants of this comparative cross-sectional study were 54 CWS and 63 children without stuttering (CWOS). All children were between 7 and 12 years old. ATP ability of the participants was measured using the Backward Masking (BM), Duration Pattern (DP), and Gap in Noise (GIN) tests. Then, the groups were compared in terms of ATP results. The correlation between the scores of these tests and stuttering severity was assessed. Results: According to the results, CWS showed poorer performance on DP, BM and GIN tests when compared with CWOS. Moreover, the stuttering severity had a significant negative correlation with the DP scores and percentage of correct identification scores in GIN; whereas it had a significant positive correlation with the BM thresholds. Conclusions: The results showed that some CWS have ATP disorder, which could exacerbate their stuttering. These findings highlighted the role of ATP disorder in stuttering.

Introduction

Stuttering is a common childhood developmental disorder, which affects speech fluency [1]. It may recover spontaneously, otherwise requires long-term intensive therapeutic sessions, which typically have adverse impacts on the psychological and social health of children and their family [2,3]. Stuttering is characterized by repetition of a syllable or a part of a syllable, prolongation of sounds, and frequent interruptions in speech [4]. The prevalence of stuttering in childhood is between 4% and 5%; about 1% of this population will continue to have persistent stuttering until adulthood [5]. The underlying cause of stuttering continues to remain unknown. According to current theories, stuttering is defined as a dynamic and multifactorial disorder, which occurs due to the effects of complex interactions between atypical speech motor control system, auditory processing, language and other cognitive processing, genetic, emotions, and social factors [6]. Auditory processing is a neural mechanism, which has been emphasized in numerous theories for its role in stuttering [7]. According to the Direction of Velocities into Articulators (DIVA) model, the auditory system has a role in activating and tuning speech sound maps and forming internal speech control models (feedforward and feedback models) through neural representation of sounds via processing others’ speech, receiving self-produced speech feedback, and interacting with motor cortex [8]. Proposing the “internal model and feedback-biased motor control theory,” Max et al. (2004) assumed that stuttering is due to a mismatch between predicted (feedforward) and actual (feedback) auditory consequences of speech.The researchers attributed this mismatch and speech error to the auditory feedback control pathway [9].

1- Introduction

2- Method

3- Results

4- Discussion

5- Conclusion and New Directions

References

Abstract

Purpose: This two-part (i.e., Study 1, Study 2) study investigated behavioral inhibition (BI) in preschool-age children who do (CWS) and do not (CWNS) stutter. The purpose of Study 1 was to develop the Short Behavioral Inhibition Scale (SBIS), a parent-report scale of BI. The purpose of Study 2 was to determine, based on the SBIS, differences in BI between CWS and CWNS, and associations between BI and CWS’s stuttering frequency, stuttering severity, speech-associated attitudes, and stutteringrelated consequences/reactions. Method: Participants in Study 1 were 225 CWS and 243 CWNS with the majority of them being included in Study 2. In Study 2, a speech sample was obtained for the calculation of stuttering frequency and severity, and the parents of a subset of CWS completed the Communication Attitude Test for Preschool and Kindergarten Children Who Stutter (Vanryckeghem & Brutten, 2007), and the Test of Childhood Stuttering Disfluency-Related Consequences Rating Scale (Gillam, Logan, & Pearson, 2009). Results: Study 1 analyses indicated that SBIS is a valid and reliable tool whose items assess a single, relatively homogeneous construct. In Study 2, CWS exhibited greater mean and extreme BI tendencies than CWNS. Also CWS with higher, compared to CWS with lower, BI presented with greater stuttering frequency, more severe stuttering, greater stuttering-related consequences, and more negative communication attitudes (for CWS older than 4 years of age). Conclusion: Findings were taken to suggest that BI is associated with early childhood stuttering and that the SBIS could be included as part of a comprehensive evaluation of stuttering.

Caveats

The principal component analysis indicated that SBIS items related to fear and reactivity had poor loadings and that a model without these items provided a better fit to the data. However, fear and reactivity have some degree of overlap with the construct of BI. Thus, the SBIS is less than a robust instrument for determining fear and reactivity components of BI, a limitation that should be noted by prospective users of SBIS. Also, some users might find the scoring of the test counterintuitive since higher scores indicate lower, rather than higher BI. Also, present SBIS results are based on parental responses, not unlike some findings resulting from other instruments used to test CWS, for example, the TOCS. Regardless, some clinicians as well as researchers may question the wisdom of relying on parental responses rather than, for example, more direct measurement of BI or BI-related behaviors like those of Choi et al (2013). Such questions are well taken but must await answers resulting from future research employing more direct measures. For the present, however, we would like to note Henderson and Wachs’s (2007) suggestion that although “parent report measures do contain some subjective parental components, available evidence indicates that these measures also contain a substantial objective component that does accurately assess children’s individual characteristics.” (p. 402).

1- Introduction

2- Method

3- Results

4- Discussion

References

Assumptions to Improve Public Attitudes

The literature reporting public misinformation about stuttering and resulting stereotypes, stigma, and discrimination against those who stutter is voluminous (cf. reviews by Boyle & Blood, 2015; Gabel, 2015; Hughes, 2015; Langevin, 2015). Almost every exploratory study of public attitudes has ended with a call for education of the public about stuttering, with the stated or unstated assumption that providing accurate information about the disorder would be sufficient to improve public attitudes (e.g., Bellegarde, Mayo, St. Louis, Mayo, 2016; Cooper & Cooper, 1985; Dorsey & Guenther, 2000; Ham, 1990; Hughes, 2015; St. Louis, Przepiórka, et al., 2014; Xing Ming, Jing, Wen, & Van Borsel, 2001; Valente, St. Louis, Leahy, Hall, & Jesus, 2017). If such information were to be made available to the public, a further and often stated assumption is that the quality of life of children and adults who stutter would be improved as a result of growing up and/or living in a more accepting and informed society.

Investigations to Improve Stuttering Attitudes

Compared to studies that stopped at documenting negative attitudes, relative few investigations have attempted, experimentally, to improve them. In her review of the extant literature in 2013, Abdalla (2015) identified 15 such studies (Abdalla & St. Louis, 2014; Coleman et al., 2013; Delaney, 2001; Flynn & St. Louis, 2011; Gottwald et al., 2011; Gottwald, Kent, St. Louis, & Hartley, 2014; Hughes, Gabel, Roseman, & Daniels, 2015; Junuzović-Žunić et al., 2015; Leahy, 1994; Langevin & Prasad, 2012; Mayo, Mayo, Gentry, & Hildebrandt, 2008; McGee, Kalinowski, & Stuart, 1996; Reichel & St. Louis, 2004; Reichel & St. Louis, 2007; Snyder, 2001). Abdalla concluded that “Studies that have attempted to ameliorate negative stereotypes toward people who stutter have been inconclusive. Some have reported positive changes, while others have found either no shift in attitude or a change in the reverse direction (i.e., intervention allegedly reinforced the negative stereotypes)” (p. 117). Factors identified by Abdalla (2015) that should be considered in evaluating the research related to changing attitudes included: (a) specific constructs targeted for change; (b) prior exposure of participants to people who stutter; (c) selection criteria for participants; (d) mode of the intervention (e.g., video, coursework, or direct interaction with a stuttering person); (e) actual content of the stimuli provided; and (f) methods of design, analysis, and interpretation. Stressing that these factors are interrelated, she noted, “While the stimuli used to change attitudes may partially explain the lack of consensus in changing attitudes of fluent speakers toward stuttering, the stimuli adopted in a study must be meaningful for the target population” (p. 124, italics added). This implies that both characteristics of the interventions as well as characteristics of the persons targeted are important in explaining the success of attitude change endeavors.

1- Introduction

2- Previous work

3- Data transcription and annotation

4- Detecting stuttering events

5- Features of the classifiers used to detect stuttering events

6- Automatic speech recognition system

7- Experiments

8- Conclusion

References

Abstract

Stuttering is a speech disorder that, if treated during childhood, may be prevented from persisting into adolescence. A clinician must first determine the severity of stuttering, assessing a child during a conversational or reading task, recording each instance of disfluency, either in real time, or after transcribing the recorded session and analysing the transcript. The current study evaluates the ability of two machine learning approaches, namely conditional random fields (CRF) and bi-directional long-short-term memory (BLSTM), to detect stuttering events in transcriptions of stuttering speech. The two approaches are compared for their performance both on ideal hand-transcribed data and also on the output of automatic speech recognition (ASR). We also study the effect of data augmentation to improve performance. A corpus of 35 speakers’ read speech (13K words) was supplemented with a corpus of 63 speakers’ spontaneous speech (11K words) and an artificially-generated corpus (50K words). Experimental results show that, without feature engineering, BLSTM classifiers outperform CRF classifiers by 33.6%. However, adding features to support the CRF classifier yields performance improvements of 45% and 18% over the CRF baseline and BLSTM results, respectively. Moreover, adding more data to train the CRF and BLSTM classifiers consistently improves the results.

Introduction

Stuttering, also known as stammering, is a speech communication disorder that can have severe social, educational and emotional maladjustment consequences, not only for the people who stutter but also for their families [1, 2]. It is presumed that early intervention is best to offset potential later impacts of having a stutter on one’s psycho-social and communication developments [3]. During the assessment phase, clinicians carefully measure the stuttering events to determine if the stuttering is normal disfluency, borderline stuttering or beginning stuttering [4]. There are several approaches to determine stuttering severity. The fluency of very young children is commonly assessed through a conversational task, whereas for children older than seven years, a reading task may be used [5, 6]. The clinician asks the child to read from a passage, and then records each instance of disfluency while the child is reading. Clearly, this process is extremely dependent on the clinician’s experience [7, 8, 9, 10].

1- Introduction

2- Methods

3- Results

4- Discussion

References

Abstract

Purpose: Numerous “small N” studies of language ability in children who stutter have produced differing conclusions. We combined test and spontaneous language data from a large cohort of children who stutter (CWS) and typically fluent peers, gathered from independent laboratories across the US, to appraise a variety of lexical measures. Method: Standardized receptive and expressive vocabulary test data and spontaneous language samples from 99 pairs of CWS (ages 25–100 months), and age-, gender-, and SES-matched children who do not stutter (CWNS) were compared. Language sample transcripts were analyzed with four measures of lexical diversity. Correlations between lexical diversity measures and expressive vocabulary scores were also calculated. Results: On standardized tests of both receptive and expressive vocabulary, there were significant differences between CWS and CWNS. In contrast, on spontaneous language measures of lexical diversity, CWS did not differ in their lexical diversity, across analyses, compared to CWNS. Three of the four lexical diversity analyses, MATTR, VocD, and NDW, were significantly correlated with each other. Conclusions: We were able to confirm prior findings of relative disadvantage on standardized vocabulary tests for a very large sample of well-matched CWS. However, spontaneous language measures of lexical diversity did not distinguish the groups. This relative weakness in CWS may emerge from task differences: CWS are free to encode their own spontaneous utterances but must comply with explicit lexical prompts in standardized testing situations.

Summary

The primary goal of this study was to examine lexical diversity in spontaneous conversational language, as well as to examine performance on standardized vocabulary testing, by pooling CWS and CWNS across seven sites/research labs within the United States. A second goal was to assess the relations among the different lexical diversity measures to explore questions of concurrent validity of MATTR. Finally, we examined the relations between lexical diversity measures and expressive vocabulary knowledge. A highly significant difference was found between CWS and CWNS on standard expressive and receptive vocabulary scores across studies that used different vocabulary measures. The CWS performed, on average, almost one standard deviation below the CWNS on the expressive vocabulary tests. Both groups still fell within the normal (nonclinical) range, however. In contrast, CWS demonstrated no significant differences in lexical diversity compared to CWNS on several lexical diversity measures in spontaneous language samples: overall TTR, TTR50, NDW, VocD, and a new measure, MATTR. Among spontaneous lexical diversity measures, there were significant correlations among NDW, VocD, and MATTR, providing some evidence of MATTR’s concurrent validity with similar measures of lexical diversity, and suggesting the importance of using the entire transcript for language sample analysis. However, none of the lexical diversity analyses were significantly correlated with children’s expressive vocabulary test score performance. We wish to point out that behavioral measures are at best a crude indicator of skill areas that may be more difficult for CWS than their typical peers. Many of the children we profiled were participants in neuroimaging studies that found atypical profiles of activation under linguistic task demand, including lexical processing (Hartfield & Conture, 2006; Kreidler, Hampton Wray, Usler, & Weber, 2017; Weber-Fox, Wray, & Arnold, 2013). Experimental tasks, such as naming following priming conditions, have also been shown to distinguish CWS from typical peers (e.g., Pellowski & Conture, 2005). Thus, work to examine either standardized test performance or language sample analysis profiles in CWS need to be part of a larger unified, multifactorial approach to understanding the nature of stuttering in early childhood. Though stuttering is, at its core, a disorder of speech motor coordination, at this point, there is a variety of converging results indicating that CWS often score below fluent, well-matched peers on tests of language skill, demonstrate neuroimaging profiles that suggest that CWS process language tasks atypically, or show exacerbation of disfluency under linguistic demand. Such findings clearly have ramifications for a full understanding of how stuttering arises, its precipitators in the speech of young children, and potential ramifications for assessment and intervention. We chose to explore these concerns by combining data to permit more robust statistical analysis of past work, and hope that others will follow in our lead.

1- Introduction

2- Method

3-  Results

4- Discussion

5- Conclusion

References

Abstract

Background: Stuttering and speech sound disorder may co-occur during early childhood, although the exact rate of comorbidity in a community-cohort sample remains unknown. In isolation, both disorders have the potential for long-term negative effects. Comorbidity rates of 16%–46% reported in previous studies were based on parent report, speech-language therapist surveys, case file audits or direct observation studies from clinical samples. Rigorous methodology utilising a prospective, longitudinal community-cohort design is required to support these previous findings. Aims: First, to identify the proportion of children with comorbid stuttering and speech sound disorder at 4 years of age drawn from a community-cohort study. Second, to compare demographic and clinical features of this comorbid diagnosis group compared to children with no diagnosis of either disorder, or those with either disorder in isolation. Methods & procedures: Participants were drawn from a prospective, longitudinal community cohort study (the Early Language in Victoria Study) at 4 years of age (n = 1607). Demographic and clinical features for comparison were theoretically driven and included: gender, birth history, feeding status, speech and language status, family history of communication difficulties, maternal education, maternal vocabulary, maternal mental health and socioeconomic status. Outcomes & results: Of the 160 children diagnosed with stuttering between 2 and 4 years of age, 6.88 % (n = 11) also had a speech sound disorder. Given the small sample size and number of comparisons performed, there was insufficient evidence to rule out that group differences observed were not simply due to chance. Conclusions & implications: The prevalence of comorbid stuttering and speech sound disorder was lower in a community cohort compared to that reported in clinical studies. Higher rates reported in clinical samples may be due to increased parental help-seeking behaviour when the two disorders co-occur. Subsequently, these children may present to clinics more frequently. Accurate representation of prevalence allows for population specific research on best practice assessment and intervention. Currently little is known about how best to manage this caseload, therefore more research is required in this area, including the determination of prognostic variables to provide efficient and effective management.

Introduction

Communication impairment in childhood is relatively common and may involve a number of domains including speech, language, fluency (e.g., stuttering), voice, and/or literacy (Speech Pathology Australia, 2016). Additionally, disorders involving these domains can co-occur. One commonly reported example is when stuttering co-occurs with speech sound disorder (SSD). Stuttering is a neurobiological disorder of communication (Drayna & Kang, 2011) which disturbs the flow of speech (Onslow et al., 2003; Sowman, Crain, Harrison, & Johnson, 2014), and typically appears between the ages of 2 and 4 years (Reilly et al., 2013). Speech sound disorder is a broad term, but here we focus on atypical phonetic or phonological speech errors, or delayed speech errors, which occur beyond age-expected norms (Morgan et al., 2017). The potential negative impact of stuttering is well documented, and can be long reaching across the lifespan (Blood & Blood, 2007; Iverach et al., 2009; Langevin, Packman, & Onslow, 2009; O’Brian, Jones, Packman, Menzies, & Onslow, 2011). For example, stuttering in the preschool years can elicit negative peer interactions (Langevin et al., 2009). In the school years, children who stutter have a heightened risk of being teased and bullied compared to their non-stuttering peers (Blood & Blood, 2007). Adults who stutter are at greater risk of developing mental health disorders (Iverach et al., 2009), and stuttering can effect educational (O’Brian et al., 2011) and vocational attainment (Klein & Hood, 2004). Similar to stuttering, having a SSD may also have negative consequences on an individual across a lifetime (Law, Boyle, Harris, Harkness, & Nye, 1998; Mccormack, Mcleod, Mcallister, & Harrison, 2009). For example, if SSDs persist into the school years, children may be at-risk for poorer literacy outcomes compared to their typical peers (Nathan, Stackhouse, Goulandris, & Snowling, 2004). Additionally, other academic skills may be impacted, as well as poorer psychosocial outcomes and vocational attainment (Mccormack et al., 2009). Given the potential negative impact across stuttering and SSD, it is important to be able to identify and better understand which children are at risk for these conditions when they co-occur to offset potential deleterious and possibly additive long-term effects. Yet the exact rate of co-occurrence of these disorders remains unknown and little is understood of potential prognostic variables in this field. It has been reported that between 16–46 % of children who stutter will also have co-occurring SSDs (Conture, Louko, & Edwards, 1993; Louko, 1995; Melnick & Conture, 2000; Ratner, 1995; Wolk, Blomgren, & Smith, 2000; Wolk, 1998). Yet the methodologies used to determine these rates in the surrounding literature have varied, and may explain the inconsistent findings (Unicomb, 2015). Three main methodological approaches employed in this field have been survey studies, retrospective file (chart) audits, and observation studies of clinical samples, discussed here in turn.

1- Introduction

2- Study 1

3- Study 2

4- General discussion

References

Abstract

Purpose: Two studies assessed implicit (Study 1) and explicit (Study 2) attitudes toward stuttering and those who stutter among speech-language pathologists (SLPs). Method: In Study 1, 15 SLPs completed the Stuttering Implicit Association Test, a measure of implicit attitudes toward stuttered speech. In Study 2, 40 SLPs provided explicit attitudes about individuals who stutter, assessed via self-report ratings of an adult who stutters and one who does not. Participants also completed measures of experience with stuttering. Results: As a group, clinicians displayed negative implicit attitudes toward stuttering. Explicit attitudes toward a person who stutters were positive, albeit less positive than attitudes toward a person who does not stutter. Amount of prior exposure to stuttering among these experienced SLPs was not significantly associated with either implicit or explicit attitudes. Conclusions: These findings highlight the importance of evaluating both implicit and explicit attitudes toward stuttering. The finding of positive explicit attitudes but negative implicit attitudes among similar samples of SLPs underscores the need to study implicit attitudes toward stuttering. Considering only explicit attitudes could lead to incomplete conclusions about the complex nature of attitudes toward stuttering.

Introduction

In Plato’s The Republic he writes, “See human beings as though they were in an underground cave-like dwelling with its entrance, a long one, open to the light across the whole width of the cave. They are in it from childhood with their legs and necks in bonds so that they are fixed, seeing only in front of them, unable because of the bond to turn their heads all the way around.” Every day the people in the caves watch shadows that were projected on a blank wall from people moving outside the cave. Because that’s all they see, the shadows shape their entire reality. Plato’s allegory likens human perception to the shadows, a representation of experience but not direct reality. Such are the effects of our attitudes, perceptions of reality filtered through the lens of our biases and previous experiences, evaluations of people, objects, or events that are stored in memory and influence the way individuals interact with the world around them (e.g., Eagly & Chaiken, 1993). Attitudes become stigmatizing when a group of individuals is associated with negative characteristics or stereotypes, leading others to discriminate against and distance themselves from individuals belonging to the group (Goffman, 1963). Negative stereotypes tend to persist because stigmatizing attitudes are difficult to change once formed (Olson & Zanna, 1993).