Analysis and Detection of Pathological Voice using Glottal Source Features

Automatic detection of voice pathology enables objective assessment and earlier intervention for the diagnosis. This study provides a systematic analysis of glottal source features and investigates their effectiveness in voice pathology detection. Glottal source features are extracted using glottal flows estimated with the quasi-closed phase (QCP) glottal inverse filtering method, using approximate glottal source signals computed with the zero frequency filtering (ZFF) method, and using acoustic voice signals directly. In addition, we propose to derive mel-frequency cepstral coefficients (MFCCs) from the glottal source waveforms computed by QCP and ZFF to effectively capture the variations in glottal source spectra of pathological voice. Experiments were carried out using two databases, the Hospital Universitario Principe de Asturias (HUPA) database and the Saarbrucken Voice Disorders (SVD) database. Analysis of features revealed that the glottal source contains information that discriminates normal and pathological voice. Pathology detection experiments were carried out using support vector machine (SVM). From the detection experiments it was observed that the performance achieved with the studied glottal source features is comparable or better than that of conventional MFCCs and perceptual linear prediction (PLP) features. The best detection performance was achieved when the glottal source features were combined with the conventional MFCCs and PLP features, which indicates the complementary nature of the features

PUHEEN TUOTTAMISEN KUVAAMINEN PARAMETROIMALLA KÄÄNTEISSUODATUKSELLA ESTIMOITU GLOTTISHERÄTE

Soinnillisen äänteen herätesignaali, värähtelevien äänihuulten välistä purkautuvaglottisheräte, voidaan estimoida käyttämällä ns. käänteissuodatusmenetelmää. Puheen tuottamisen analyysi muodostuu tällöin tyypillisesti kahdestavaiheesta: (a) glottisherätteen laskennasta käänteissuodatuksella ja (b) saatujenvirtauspulssijonojen parametroinnista. Jälkimmäisen vaiheen tarkoitus onkuvata puheen tuoton herätesignaalin oleellisin informaatio numeerisessamuodossa. Tässä artikkelissa tarkastellaan niitä menetelmiä, joita on kehitettyglottisherätteen parametrointiin. Menetelmät kuvataan jakamalla ne aika- jataajuusalueen tekniikoihin, ja jokaisen parametrin kohdalla on koostettutietoa niiden käyttösovellutuksista ja tyypillisistä arvoista. Lopuksi vertailIaantunnetuimpien tekniikoiden käytettävyyttä äänitutkimuksessa.Avainsanat: puheen tuottaminen, käänteissuodatus, glottisheräte, parametrointiEstimation of the source ofvoiced speech, the glottal volume velocity waveform, withinverse filtering involves usually a parameterisation stage, where the obtained flowwaveforms are expressed in numerical form. This stage of the voice source analysis, theparameterisation of the glottal flow, is discussed in the present paper. The paper aims togive a review of the different methods developed for the parameterisation and it discusseshow these parameters have reflected the function of the voice source in various voiceproduction studies.Keywords: speech production, inverse filtering, glottal excitation, parameterisatio

Refining a Deep Learning-based Formant Tracker using Linear Prediction Methods

In this study, formant tracking is investigated by refining the formants tracked by an existing data-driven tracker, DeepFormants, using the formants estimated in a model-driven manner by linear prediction (LP)-based methods. As LP-based formant estimation methods, conventional covariance analysis (LP-COV) and the recently proposed quasi-closed phase forward-backward (QCP-FB) analysis are used. In the proposed refinement approach, the contours of the three lowest formants are first predicted by the data-driven DeepFormants tracker, and the predicted formants are replaced frame-wise with local spectral peaks shown by the model-driven LP-based methods. The refinement procedure can be plugged into the DeepFormants tracker with no need for any new data learning. Two refined DeepFormants trackers were compared with the original DeepFormants and with five known traditional trackers using the popular vocal tract resonance (VTR) corpus. The results indicated that the data-driven DeepFormants trackers outperformed the conventional trackers and that the best performance was obtained by refining the formants predicted by DeepFormants using QCP-FB analysis. In addition, by tracking formants using VTR speech that was corrupted by additive noise, the study showed that the refined DeepFormants trackers were more resilient to noise than the reference trackers. In general, these results suggest that LP-based model-driven approaches, which have traditionally been used in formant estimation, can be combined with a modern data-driven tracker easily with no further training to improve the tracker's performance.Comment: Computer Speech and Language, Vol. 81, Article 101515, June 202

Severity Classification of Parkinson's Disease from Speech using Single Frequency Filtering-based Features

Developing objective methods for assessing the severity of Parkinson's disease (PD) is crucial for improving the diagnosis and treatment. This study proposes two sets of novel features derived from the single frequency filtering (SFF) method: (1) SFF cepstral coefficients (SFFCC) and (2) MFCCs from the SFF (MFCC-SFF) for the severity classification of PD. Prior studies have demonstrated that SFF offers greater spectro-temporal resolution compared to the short-time Fourier transform. The study uses the PC-GITA database, which includes speech of PD patients and healthy controls produced in three speaking tasks (vowels, sentences, text reading). Experiments using the SVM classifier revealed that the proposed features outperformed the conventional MFCCs in all three speaking tasks. The proposed SFFCC and MFCC-SFF features gave a relative improvement of 5.8% and 2.3% for the vowel task, 7.0% & 1.8% for the sentence task, and 2.4% and 1.1% for the read text task, in comparison to MFCC features.Comment: Accepted by INTERSPEECH 202

Disentangling the effects of phonation and articulation: Hemispheric asymmetries in the auditory N1m response of the human brain

BACKGROUND: The cortical activity underlying the perception of vowel identity has typically been addressed by manipulating the first and second formant frequency (F1 & F2) of the speech stimuli. These two values, originating from articulation, are already sufficient for the phonetic characterization of vowel category. In the present study, we investigated how the spectral cues caused by articulation are reflected in cortical speech processing when combined with phonation, the other major part of speech production manifested as the fundamental frequency (F0) and its harmonic integer multiples. To study the combined effects of articulation and phonation we presented vowels with either high (/a/) or low (/u/) formant frequencies which were driven by three different types of excitation: a natural periodic pulseform reflecting the vibration of the vocal folds, an aperiodic noise excitation, or a tonal waveform. The auditory N1m response was recorded with whole-head magnetoencephalography (MEG) from ten human subjects in order to resolve whether brain events reflecting articulation and phonation are specific to the left or right hemisphere of the human brain. RESULTS: The N1m responses for the six stimulus types displayed a considerable dynamic range of 115–135 ms, and were elicited faster (~10 ms) by the high-formant /a/ than by the low-formant /u/, indicating an effect of articulation. While excitation type had no effect on the latency of the right-hemispheric N1m, the left-hemispheric N1m elicited by the tonally excited /a/ was some 10 ms earlier than that elicited by the periodic and the aperiodic excitation. The amplitude of the N1m in both hemispheres was systematically stronger to stimulation with natural periodic excitation. Also, stimulus type had a marked (up to 7 mm) effect on the source location of the N1m, with periodic excitation resulting in more anterior sources than aperiodic and tonal excitation. CONCLUSION: The auditory brain areas of the two hemispheres exhibit differential tuning to natural speech signals, observable already in the passive recording condition. The variations in the latency and strength of the auditory N1m response can be traced back to the spectral structure of the stimuli. More specifically, the combined effects of the harmonic comb structure originating from the natural voice excitation caused by the fluctuating vocal folds and the location of the formant frequencies originating from the vocal tract leads to asymmetric behaviour of the left and right hemisphere

Parameterization of a computational physical model for glottal flow using inverse filtering and high-speed videoendoscopy

High-speed videoendoscopy, glottal inverse filtering, and physical modeling can be used to obtain complementary information about speech production. In this study, the three methodologies are combined to pursue a better understanding of the relationship between the glottal air flow and glottal area. Simultaneously acquired high-speed video and glottal inverse filtering data from three male and three female speakers were used. Significant correlations were found between the quasi-open and quasi-speed quotients of the glottal area (extracted from the high-speed videos) and glottal flow (estimated using glottal inverse filtering), but only the quasi-open quotient relationship could be represented as a linear model. A simple physical glottal flow model with three different glottal geometries was optimized to match the data. The results indicate that glottal flow skewing can be modeled using an inertial vocal/subglottal tract load and that estimated inertia within the glottis is sensitive to the quality of the data. Parameter optimisation also appears to favour combining the simplest glottal geometry with viscous losses and the more complex glottal geometries with entrance/exit effects in the glottis.Peer reviewe

Analysis of phonation onsets in vowel production, using information from glottal area and flow estimate

A multichannel dataset comprising high-speed videoendoscopy images, and electroglottography and free-field microphone signals, was used to investigate phonation onsets in vowel production. Use of the multichannel data enabled simultaneous analysis of the two main aspects of phonation, glottal area, extracted from the high-speed videoendoscopy images, and glottal flow, estimated from the microphone signal using glottal inverse filtering. Pulse-wise parameterization of the glottal area and glottal flow indicate that there is no single dominant way to initiate quasi-stable phonation. The trajectories of fundamental frequency and normalized amplitude quotient, extracted from glottal area and estimated flow, may differ markedly during onsets. The location and steepness of the amplitude envelopes of the two signals were observed to be closely related, and quantitative analysis supported the hypothesis that glottal area and flow do not carry essentially different amplitude information during vowel onsets. Linear models were used to predict the phonation onset times from the characteristics of the subsequent steady phonation. The phonation onset time of glottal area was found to have good predictability from a combination of the fundamental frequency and the normalized amplitude quotient of the glottal flow, as well as the gender of the speaker. For the phonation onset time of glottal flow, the best linear model was obtained using the fundamental frequency and the normalized amplitude quotient of the glottal flow as predictors.Peer reviewe

Reducing mismatch in training of DNN-based glottal excitation models in a statistical parametric text-to-speech system

Neural network-based models that generate glottal excitation waveforms from acoustic features have been found to give improved quality in statistical parametric speech synthesis. Until now, however, these models have been trained separately from the acoustic model. This creates mismatch between training and synthesis, as the synthesized acoustic features used for the excitation model input differ from the original inputs, with which the model was trained on. Furthermore, due to the errors in predicting the vocal tract filter, the original excitation waveforms do not provide perfect reconstruction of the speech waveform even if predicted without error. To address these issues and to make the excitation model more robust against errors in acoustic modeling, this paper proposes two modifications to the excitation model training scheme. First, the excitation model is trained in a connected manner, with inputs generated by the acoustic model. Second, the target glottal waveforms are re-estimated by performing glottal inverse filtering with the predicted vocal tract filters. The results show that both of these modifications improve performance measured in MSE and MFCC distortion, and slightly improve the subjective quality of the synthetic speech.Peer reviewe