A novel framework for high-quality voice source analysis and synthesis

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The analysis, parameterization and modeling of voice source estimates obtained via inverse filtering of recorded speech are some of the most challenging areas of speech processing owing to the fact humans produce a wide range of voice source realizations and that the voice source estimates commonly contain artifacts due to the non-linear time-varying source-filter coupling. Currently, the most widely adopted representation of voice source signal is Liljencrants-Fant's (LF) model which was developed in late 1985. Due to the overly simplistic interpretation of voice source dynamics, LF model can not represent the fine temporal structure of glottal flow derivative realizations nor can it carry the sufficient spectral richness to facilitate a truly natural sounding speech synthesis. In this thesis we have introduced Characteristic Glottal Pulse Waveform Parameterization and Modeling (CGPWPM) which constitutes an entirely novel framework for voice source analysis, parameterization and reconstruction. In comparative evaluation of CGPWPM and LF model we have demonstrated that the proposed method is able to preserve higher levels of speaker dependant information from the voice source estimates and realize a more natural sounding speech synthesis. In general, we have shown that CGPWPM-based speech synthesis rates highly on the scale of absolute perceptual acceptability and that speech signals are faithfully reconstructed on consistent basis, across speakers, gender. We have applied CGPWPM to voice quality profiling and text-independent voice quality conversion method. The proposed voice conversion method is able to achieve the desired perceptual effects and the modified speech remained as natural sounding and intelligible as natural speech. In this thesis, we have also developed an optimal wavelet thresholding strategy for voice source signals which is able to suppress aspiration noise and still retain both the slow and the rapid variations in the voice source estimate

New Excitation Signal for High Quality Linear Predictive Coding Speech Synthesis

The purpose of this thesis is to improve the quality of synthetic to attempt to speech by using the bes t excitation signal during Linear Predictive Coding (LPC) synthesis. This thesis examines the human speech system as a basis for our synthetic speech model. Then it closely examines LPC synthesis, including the mathematical details. One dominant factor in producing natural-sounding and intelligible speech is the excitation signal. For LPC synthesis the excitation signal must have a flat frequency spectrum. A train of impulses separated by the pitch period of the speech has been the standard excitation signal for voiced speech in LPC synthesis. Unfortunately, speech produced using this excitation signal has an unnatural nbuzz . For natural-sounding speech, the excitation signal should resemble the glottal volume velocity waveform. The glottal volume velocity waveform is a measure of the excitation that produces natural speech and it does not have a flat frequency spectrum. This raises the question: what type of excitation signal should be used to produce the most natural-sounding speech possible? To answer this question, we examined six excitation signals that are currently being used in LPC synthesis. We also developed many new excitation signals to be used specifically for synthesizing natural-sounding speech. We experimented with the LPC parameters and these excitation signals to determine the conditions that produced the best speech. Then we compared five of the excitation signals in forced pair trials. We found that our new excitation, LF Impulse excitation, produced speech superior in overall quality (that is naturalness and intelligibility) to the others. We conclude, therefore, that LF Impulse excitation, or an excitation similar to it, should be considered when attempting to produce speech that is both natural-sounding and intelligible with LPC synthesis

Speech synthesis based on a harmonic model

The wide range of potential commercial applications for a com puter system capable of automatically converting text to speech (TTS) has stimulated decades of research. One of the currently most successful approaches to synthesising speech, concatenative TTS synthesis, combines prerecorded speech units to build full utterances. However, th e prosody of the stored units is often not consistent with that of the target utterance and m ust be altered. Furthermore, several types of mismatch can occur at unit boundaries and must be smoothed. Thus, pitch and time-scale modification techniques as well as smoothing algorithms play a critical role in all concatenative-based systems. This thesis presents the developm ent of a concatenative TTS system based on a harm onic model and incorporating new pitch and time-scaling as well as smoothing algorithms. Experim ent has shown our system capable of both very high quality prosodic modification and synthesis. Results com pare very favourably with those of existing state-of-the-art systems

Analysis and correction of the helium speech effect by autoregressive signal processing

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