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

Acoustic measurement of overall voice quality in sustained vowels and continuous speech

Measurement of dysphonia severity involves auditory-perceptual evaluations and acoustic analyses of sound waves. Meta-analysis of proportional associations between these two methods showed that many popular perturbation metrics and noise-to-harmonics and others ratios do not yield reasonable results. However, this meta-analysis demonstrated that the validity of specific autocorrelation- and cepstrum-based measures was much more convincing, and appointed ‘smoothed cepstral peak prominence’ as the most promising metric of dysphonia severity. Original research confirmed this inferiority of perturbation measures and superiority of cepstral indices in dysphonia measurement of laryngeal-vocal and tracheoesophageal voice samples. However, to be truly representative for daily voice use patterns, measurement of overall voice quality is ideally founded on the analysis of sustained vowels ánd continuous speech. A customized method for including both sample types and calculating the multivariate Acoustic Voice Quality Index (i.e., AVQI), was constructed for this purpose. Original study of the AVQI revealed acceptable results in terms of initial concurrent validity, diagnostic precision, internal and external cross-validity and responsiveness to change. It thus was concluded that the AVQI can track changes in dysphonia severity across the voice therapy process. There are many freely and commercially available computer programs and systems for acoustic metrics of dysphonia severity. We investigated agreements and differences between two commonly available programs (i.e., Praat and Multi-Dimensional Voice Program) and systems. The results indicated that clinicians better not compare frequency perturbation data across systems and programs and amplitude perturbation data across systems. Finally, acoustic information can also be utilized as a biofeedback modality during voice exercises. Based on a systematic literature review, it was cautiously concluded that acoustic biofeedback can be a valuable tool in the treatment of phonatory disorders. When applied with caution, acoustic algorithms (particularly cepstrum-based measures and AVQI) have merited a special role in assessment and/or treatment of dysphonia severity

Glottal-synchronous speech processing

Glottal-synchronous speech processing is a field of speech science where the pseudoperiodicity of voiced speech is exploited. Traditionally, speech processing involves segmenting and processing short speech frames of predefined length; this may fail to exploit the inherent periodic structure of voiced speech which glottal-synchronous speech frames have the potential to harness. Glottal-synchronous frames are often derived from the glottal closure instants (GCIs) and glottal opening instants (GOIs). The SIGMA algorithm was developed for the detection of GCIs and GOIs from the Electroglottograph signal with a measured accuracy of up to 99.59%. For GCI and GOI detection from speech signals, the YAGA algorithm provides a measured accuracy of up to 99.84%. Multichannel speech-based approaches are shown to be more robust to reverberation than single-channel algorithms. The GCIs are applied to real-world applications including speech dereverberation, where SNR is improved by up to 5 dB, and to prosodic manipulation where the importance of voicing detection in glottal-synchronous algorithms is demonstrated by subjective testing. The GCIs are further exploited in a new area of data-driven speech modelling, providing new insights into speech production and a set of tools to aid deployment into real-world applications. The technique is shown to be applicable in areas of speech coding, identification and artificial bandwidth extension of telephone speec

Exploring pause fillers in conversational speech for forensic phonetics: findings in a Spanish cohort including twins

Pause fillers occur naturally during conversational speech, and have recently generated interest in their use for forensic applications. We extracted pause fillers from conversational speech from 54 speakers, including twins, whose voices are often perceptually similar. Overall 872 tokens of the sound [e:] were extracted (7-33 tokens per speaker), and objectively characterised using 315 acoustic measures. We used a Random Forest (RF) classifier and tested its performance using a leaveone- sample-out scheme to obtain probabilistic estimates of binary class membership denoting whether a query token belongs to a speaker. We report results using the Receiver Operating Characteristic (ROC) curve, and computing the Area Under the Curve (AUC). When the RF was presented with at least 20 tokens in the training phase for each of the two classes, we observed AUC in the range 0.71-0.98. These findings have important implications in the potential of pause fillers as an additional objective tool in forensic speaker verification

Acoustic and videoendoscopic techniques to improve voice assessment via relative fundamental frequency

Quantitative measures of laryngeal muscle tension are needed to improve assessment and track clinical progress. Although relative fundamental frequency (RFF) shows promise as an acoustic estimate of laryngeal muscle tension, it is not yet transferable to the clinic. The purpose of this work was to refine algorithmic estimation of RFF, as well as to enhance the knowledge surrounding the physiological underpinnings of RFF. The first study used a large database of voice samples collected from 227 speakers with voice disorders and 256 typical speakers to evaluate the effects of fundamental frequency estimation techniques and voice sample characteristics on algorithmic RFF estimation. By refining fundamental frequency estimation using the Auditory Sawtooth Waveform Inspired Pitch Estimator—Prime (Auditory-SWIPE′) algorithm and accounting for sample characteristics via the acoustic measure, pitch strength, algorithmic errors related to the accuracy and precision of RFF were reduced by 88.4% and 17.3%, respectively. The second study sought to characterize the physiological factors influencing acoustic outputs of RFF estimation. A group of 53 speakers with voice disorders and 69 typical speakers each produced the utterance, /ifi/, while simultaneous recordings were collected using a microphone and flexible nasendoscope. Acoustic features calculated via the microphone signal were examined in reference to the physiological initiation and termination of vocal fold vibration. The features that corresponded with these transitions were then implemented into the RFF algorithm, leading to significant improvements in the precision of the RFF algorithm to reflect the underlying physiological mechanisms for voicing offsets (p < .001, V = .60) and onsets (p < .001, V = .54) when compared to manual RFF estimation. The third study further elucidated the physiological underpinnings of RFF by examining the contribution of vocal fold abduction to RFF during intervocalic voicing offsets. Vocal fold abductory patterns were compared to RFF values in a subset of speakers from the second study, comprising young adults, older adults, and older adults with Parkinson’s disease. Abductory patterns were not significantly different among the three groups; however, vocal fold abduction was observed to play a significant role in measures of RFF at voicing offset. By improving algorithmic estimation and elucidating aspects of the underlying physiology affecting RFF, this work adds to the utility of RFF for use in conjunction with current clinical techniques to assess laryngeal muscle tension.2021-09-29T00:00:00

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies