Identification of Transient Speech Using Wavelet Transforms

It is generally believed that abrupt stimulus changes, which in speech may be time-varying frequency edges associated with consonants, transitions between consonants and vowels and transitions within vowels are critical to the perception of speech by humans and for speech recognition by machines. Noise affects speech transitions more than it affects quasi-steady-state speech. I believe that identifying and selectively amplifying speech transitions may enhance the intelligibility of speech in noisy conditions. The purpose of this study is to evaluate the use of wavelet transforms to identify speech transitions. Using wavelet transforms may be computationally efficient and allow for real-time applications. The discrete wavelet transform (DWT), stationary wavelet transform (SWT) and wavelet packets (WP) are evaluated. Wavelet analysis is combined with variable frame rate processing to improve the identification process. Variable frame rate can identify time segments when speech feature vectors are changing rapidly and when they are relatively stationary. Energy profiles for words, which show the energy in each node of a speech signal decomposed using wavelets, are used to identify nodes that include predominately transient information and nodes that include predominately quasi-steady-state information, and these are used to synthesize transient and quasi-steady-state speech components. These speech components are estimates of the tonal and nontonal speech components, which Yoo et al identified using time-varying band-pass filters. Comparison of spectra, a listening test and mean-squared-errors between the transient components synthesized using wavelets and Yoo's nontonal components indicated that wavelet packets identified the best estimates of Yoo's components. An algorithm that incorporates variable frame rate analysis into wavelet packet analysis is proposed. The development of this algorithm involves the processes of choosing a wavelet function and a decomposition level to be used. The algorithm itself has 4 steps: wavelet packet decomposition; classification of terminal nodes; incorporation of variable frame rate processing; synthesis of speech components. Combining wavelet analysis with variable frame rate analysis provides the best estimates of Yoo's speech components

ENHANCEMENT OF SPEECH INTELLIGIBILITY USING SPEECH TRANSIENTS EXTRACTED BY A WAVELET PACKET-BASED REAL-TIME ALGORITHM

Studies have shown that transient speech, which is associated with consonants, transitions between consonants and vowels, and transitions within some vowels, is an important cue for identifying and discriminating speech sounds. However, compared to the relatively steady-state vowel segments of speech, transient speech has much lower energy and thus is easily masked by background noise. Emphasis of transient speech can improve the intelligibility of speech in background noise, but methods to demonstrate this improvement have either identified transient speech manually or proposed algorithms that cannot be implemented to run in real-time.We have developed an algorithm to automatically extract transient speech in real-time. The algorithm involves the use of a function, which we term the transitivity function, to characterize the rate of change of wavelet coefficients of a wavelet packet transform representation of a speech signal. The transitivity function is large and positive when a signal is changing rapidly and small when a signal is in steady state. Two different definitions of the transitivity function, one based on the short-time energy and the other on Mel-frequency cepstral coefficients, were evaluated experimentally, and the MFCC-based transitivity function produced better results. The extracted transient speech signal is used to create modified speech by combining it with original speech.To facilitate comparison of our transient and modified speech to speech processed using methods proposed by other researcher to emphasize transients, we developed three indices. The indices are used to characterize the extent to which a speech modification/processing method emphasizes (1) a particular region of speech, (2) consonants relative to, and (3) onsets and offsets of formants compared to steady formant. These indices are very useful because they quantify differences in speech signals that are difficult to show using spectrograms, spectra and time-domain waveforms.The transient extraction algorithm includes parameters which when varied influence the intelligibility of the extracted transient speech. The best values for these parameters were selected using psycho-acoustic testing. Measurements of speech intelligibility in background noise using psycho-acoustic testing showed that modified speech was more intelligible than original speech, especially at high noise levels (-20 and -15 dB). The incorporation of a method that automatically identifies and boosts unvoiced speech into the algorithm was evaluated and showed that this method does not result in additional speech intelligibility improvements