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

HMM-based speech synthesis using an acoustic glottal source model

Parametric speech synthesis has received increased attention in recent years following the development of statistical HMM-based speech synthesis. However, the speech produced using this method still does not sound as natural as human speech and there is limited parametric flexibility to replicate voice quality aspects, such as breathiness. The hypothesis of this thesis is that speech naturalness and voice quality can be more accurately replicated by a HMM-based speech synthesiser using an acoustic glottal source model, the Liljencrants-Fant (LF) model, to represent the source component of speech instead of the traditional impulse train. Two different analysis-synthesis methods were developed during this thesis, in order to integrate the LF-model into a baseline HMM-based speech synthesiser, which is based on the popular HTS system and uses the STRAIGHT vocoder. The first method, which is called Glottal Post-Filtering (GPF), consists of passing a chosen LF-model signal through a glottal post-filter to obtain the source signal and then generating speech, by passing this source signal through the spectral envelope filter. The system which uses the GPF method (HTS-GPF system) is similar to the baseline system, but it uses a different source signal instead of the impulse train used by STRAIGHT. The second method, called Glottal Spectral Separation (GSS), generates speech by passing the LF-model signal through the vocal tract filter. The major advantage of the synthesiser which incorporates the GSS method, named HTS-LF, is that the acoustic properties of the LF-model parameters are automatically learnt by the HMMs. In this thesis, an initial perceptual experiment was conducted to compare the LFmodel to the impulse train. The results showed that the LF-model was significantly better, both in terms of speech naturalness and replication of two basic voice qualities (breathy and tense). In a second perceptual evaluation, the HTS-LF system was better than the baseline system, although the difference between the two had been expected to be more significant. A third experiment was conducted to evaluate the HTS-GPF system and an improved HTS-LF system, in terms of speech naturalness, voice similarity and intelligibility. The results showed that the HTS-GPF system performed similarly to the baseline. However, the HTS-LF system was significantly outperformed by the baseline. Finally, acoustic measurements were performed on the synthetic speech to investigate the speech distortion in the HTS-LF system. The results indicated that a problem in replicating the rapid variations of the vocal tract filter parameters at transitions between voiced and unvoiced sounds is the most significant cause of speech distortion. This problem encourages future work to further improve the system

Voice source characterization for prosodic and spectral manipulation

The objective of this dissertation is to study and develop techniques to decompose the speech signal into its two main components: voice source and vocal tract. Our main efforts are on the glottal pulse analysis and characterization. We want to explore the utility of this model in different areas of speech processing: speech synthesis, voice conversion or emotion detection among others. Thus, we will study different techniques for prosodic and spectral manipulation. One of our requirements is that the methods should be robust enough to work with the large databases typical of speech synthesis. We use a speech production model in which the glottal flow produced by the vibrating vocal folds goes through the vocal (and nasal) tract cavities and its radiated by the lips. Removing the effect of the vocal tract from the speech signal to obtain the glottal pulse is known as inverse filtering. We use a parametric model fo the glottal pulse directly in the source-filter decomposition phase. In order to validate the accuracy of the parametrization algorithm, we designed a synthetic corpus using LF glottal parameters reported in the literature, complemented with our own results from the vowel database. The results show that our method gives satisfactory results in a wide range of glottal configurations and at different levels of SNR. Our method using the whitened residual compared favorably to this reference, achieving high quality ratings (Good-Excellent). Our full parametrized system scored lower than the other two ranking in third place, but still higher than the acceptance threshold (Fair-Good). Next we proposed two methods for prosody modification, one for each of the residual representations explained above. The first method used our full parametrization system and frame interpolation to perform the desired changes in pitch and duration. The second method used resampling on the residual waveform and a frame selection technique to generate a new sequence of frames to be synthesized. The results showed that both methods are rated similarly (Fair-Good) and that more work is needed in order to achieve quality levels similar to the reference methods. As part of this dissertation, we have studied the application of our models in three different areas: voice conversion, voice quality analysis and emotion recognition. We have included our speech production model in a reference voice conversion system, to evaluate the impact of our parametrization in this task. The results showed that the evaluators preferred our method over the original one, rating it with a higher score in the MOS scale. To study the voice quality, we recorded a small database consisting of isolated, sustained Spanish vowels in four different phonations (modal, rough, creaky and falsetto) and were later also used in our study of voice quality. Comparing the results with those reported in the literature, we found them to generally agree with previous findings. Some differences existed, but they could be attributed to the difficulties in comparing voice qualities produced by different speakers. At the same time we conducted experiments in the field of voice quality identification, with very good results. We have also evaluated the performance of an automatic emotion classifier based on GMM using glottal measures. For each emotion, we have trained an specific model using different features, comparing our parametrization to a baseline system using spectral and prosodic characteristics. The results of the test were very satisfactory, showing a relative error reduction of more than 20% with respect to the baseline system. The accuracy of the different emotions detection was also high, improving the results of previously reported works using the same database. Overall, we can conclude that the glottal source parameters extracted using our algorithm have a positive impact in the field of automatic emotion classification

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

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.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Analysis of a Modern Voice Morphing Approach using Gaussian Mixture Models for Laryngectomees

This paper proposes a voice morphing system for people suffering from Laryngectomy, which is the surgical removal of all or part of the larynx or the voice box, particularly performed in cases of laryngeal cancer. A primitive method of achieving voice morphing is by extracting the source's vocal coefficients and then converting them into the target speaker's vocal parameters. In this paper, we deploy Gaussian Mixture Models (GMM) for mapping the coefficients from source to destination. However, the use of the traditional/conventional GMM-based mapping approach results in the problem of over-smoothening of the converted voice. Thus, we hereby propose a unique method to perform efficient voice morphing and conversion based on GMM,which overcomes the traditional-method effects of over-smoothening. It uses a technique of glottal waveform separation and prediction of excitations and hence the result shows that not only over-smoothening is eliminated but also the transformed vocal tract parameters match with the target. Moreover, the synthesized speech thus obtained is found to be of a sufficiently high quality. Thus, voice morphing based on a unique GMM approach has been proposed and also critically evaluated based on various subjective and objective evaluation parameters. Further, an application of voice morphing for Laryngectomees which deploys this unique approach has been recommended by this paper.Comment: 6 pages, 4 figures, 4 tables; International Journal of Computer Applications Volume 49, Number 21, July 201