COMPARING ACOUSTIC GLOTTAL FEATURE EXTRACTION METHODS WITH SIMULTANEOUSLY RECORDED HIGH-SPEED VIDEO FEATURES FOR CLINICALLY OBTAINED DATA

Accurate methods for glottal feature extraction include the use of high-speed video imaging (HSVI). There have been previous attempts to extract these features with the acoustic recording. However, none of these methods compare their results with an objective method, such as HSVI. This thesis tests these acoustic methods against a large diverse population of 46 subjects. Two previously studied acoustic methods, as well as one introduced in this thesis, were compared against two video methods, area and displacement for open quotient (OQ) estimation. The area comparison proved to be somewhat ambiguous and challenging due to thresholding effects. The displacement comparison, which is based on glottal edge tracking, proved to be a more robust comparison method than the area. The first acoustic methods OQ estimate had a relatively small average error of 8.90% and the second method had a relatively large average error of -59.05% compared to the displacement OQ. The newly proposed method had a relatively small error of -13.75% when compared to the displacements OQ. There was some success even though there was relatively high error with the acoustic methods, however, they may be utilized to augment the features collected by HSVI for a more accurate glottal feature estimation

Extraction of vocal-tract system characteristics from speechsignals

We propose methods to track natural variations in the characteristics of the vocal-tract system from speech signals. We are especially interested in the cases where these characteristics vary over time, as happens in dynamic sounds such as consonant-vowel transitions. We show that the selection of appropriate analysis segments is crucial in these methods, and we propose a selection based on estimated instants of significant excitation. These instants are obtained by a method based on the average group-delay property of minimum-phase signals. In voiced speech, they correspond to the instants of glottal closure. The vocal-tract system is characterized by its formant parameters, which are extracted from the analysis segments. Because the segments are always at the same relative position in each pitch period, in voiced speech the extracted formants are consistent across successive pitch periods. We demonstrate the results of the analysis for several difficult cases of speech signals

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

Glottal flow characteristics in vowels produced by speakers with heart failure

Heart failure (HF) is one of the most life-threatening diseases globally. HF is an under-diagnosed condition, and more screening tools are needed to detect it. A few recent studies have suggested that HF also affects the functioning of the speech production mechanism by causing generation of edema in the vocal folds and by impairing the lung function. It has not yet been studied whether these possible effects of HF on the speech production mechanism are large enough to cause acoustically measurable differences to distinguish speech produced in HF from that produced by healthy speakers. Therefore, the goal of the present study was to compare speech production between HF patients and healthy controls by focusing on the excitation signal generated at the level of the vocal folds, the glottal flow. The glottal flow was computed from speech using the quasi-closed phase glottal inverse filtering method and the estimated flow was parameterized with 12 glottal parameters. The sound pressure level (SPL) was measured from speech as an additional parameter. The statistical analyses conducted on the parameters indicated that most of the glottal parameters and SPL were significantly different between the HF patients and healthy controls. The results showed that the HF patients generally produced a more rounded glottal pulse and a lower SPL level compared to the healthy controls, indicating incomplete glottal closure and inappropriate leakage of air through the glottis. The results observed in this preliminary study indicate that glottal features are capable of distinguishing speakers with HF from healthy controls. Therefore, the study suggests that glottal features constitute a potential feature extraction approach which should be taken into account in future large-scale investigations in studying the automatic detection of HF from speech.Peer reviewe

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