Abstract

The spectral-based features, typically used in Automatic Speech Recognition (ASR) systems, reject the phase information of speech signals. Thus, employing extra features, in which the phase of the signal is not rejected, may fill this gap. Embedding the speech signal in the Reconstructed Phase Space (RPS) and then extracting some useful features from it, is a recently considered approach in this field. In this paper, we will follow this approach by evaluating some useful features from the Recurrence Plot (RP) of the embedded speech signals in the RPS; the proposed features are evaluated via applying a two-dimensional wavelet transform to the resulted RP diagrams. The proposed features are examined in an ASR task alone and in combination with the traditional Mel-Frequency Cepstral Coefficients (MFCC). For the second case, using English TIMIT corpus, 3.94% absolute classification accuracy improvement in the phoneme recognition accuracy rate, against using only the MFCC features is gained.

Introduction

In recent decades, a variety of linear models for speech coding, synthesis, and recognition with acceptable performances have been introduced. In this way, many types of research achieved improvement in the field of speech recognition by employing novel methods [1,2] or the detection of mispronunciation using Hidden Markov model [3]; however, there are nonlinear aerodynamic phenomena in the human speech production system which generally could not be included in linear models [4]. Therefore, nonlinear methods could potentially provide effective computational models to extract acoustic features which are useful for the nonlinear phenomena detection [4]. Furthermore, some recent studies have shown that utilizing nonlinear characteristics may improve the performances of the ASR systems [5].

Usual ASR systems exploit frequency domain features like Mel frequency cepstral coefficients [6]. The traditional frequency domain methods typically extract only the first and second order properties from the spectral patterns of speech signals [7]. However, there are many signals produced via nonlinear differential equations that have wide spectral characteristics [8]. In such cases, the frequency domain techniques are deficient, because it is impossible to dissociate the information of such a signal only in the frequency domain [5].