Speech Enhancement using Transient Speech Components

We believe that the auditory system, like the visual system, may besensitive to abrupt stimulus changes and the transient component inspeech may be particularly critical to speech perception. If thiscomponent can be identified and selectively amplified, improvedspeech perception in background noise may be possible.This project describes a method to decompose speech into tonal,transient, and residual components. The modified discrete cosinetransform (MDCT) and the wavelet transform are transforms used tocapture tonal and transient features in speech. The tonal andtransient components were identified by using a small number of MDCTand wavelet coefficients, respectively. In previous studies, all ofthe MDCT and all of the wavelet coefficients were assumed to beindependent, and identifications of the significant MDCT and thesignificant wavelet coefficients were achieved by thresholds.However, an appropriate threshold is not known and the MDCT and thewavelet coefficients show statistical dependencies, described by theclustering and persistence properties.In this work, the hidden Markov chain (HMC) model and the hiddenMarkov tree (HMT) model were applied to describe the clustering andpersistence properties between the MDCT coefficients and between thewavelet coefficients. The MDCT coefficients in each frequency indexwere modeled as a two-state mixture of two univariate Gaussiandistributions. The wavelet coefficients in each scale of each treewere modeled as a two-state mixture of two univariate Gaussiandistributions. The initial parameters of Gaussian mixtures wereestimated by the greedy EM algorithm. By utilizing the Viterbi andthe MAP algorithms used to find the optimal state distribution, thesignificant MDCT and the significant wavelet coefficients weredetermined without relying on a threshold.The transient component isolated by our method was selectivelyamplified and recombined with the original speech to generateenhanced speech, with energy adjusted to equal to the energy of theoriginal speech. The intelligibility of the original and enhancedspeech was evaluated in eleven human subjects using the modifiedrhyme protocol. Word recognition rate results show that theenhanced speech can improve speech intelligibility at low SNR levels(8% at -15 dB, 14% at -20dB, and 18% at -25 dB)

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