Mel-Frequency Cepstral Coefficients of Voice Source Waveforms for Classification of Phonation Types in Speech

Kadiri, Sudarsana Reddy; Alku, Paavo

doi:10.21437/interspeech.2019-2863

Cited by 12 publications

(7 citation statements)

References 28 publications

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“…It can be seen that there are large variations especially in the harmonic structure of the glottal flow spectra between normal and pathological voice. In order to capture these variations and to represent them in a compact form, we propose to derive MFCCs from the spectra of the glottal source waveforms (as in our recent conference paper [99]). It should be noted that the proposed MFCC feature extraction is similar to the computation of conventional MFCC features, except that the proposed approach operates on the glottal source waveform instead of the acoustic voice signal.…”

Section: Extraction Of Mfccs From Glottal Source Waveformsmentioning

confidence: 99%

Analysis and Detection of Pathological Voice Using Glottal Source Features

Kadiri

Alku

2020

IEEE J. Sel. Top. Signal Process.

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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 Príncipe de Asturias (HUPA) database and the Saarbrücken 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.

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Section: Extraction Of Mfccs From Glottal Source Waveformsmentioning

confidence: 99%

Analysis and Detection of Pathological Voice Using Glottal Source Features

Kadiri

Alku

2020

IEEE J. Sel. Top. Signal Process.

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“…Here, 2M + 1 is the number of samples used to remove the trend. The ZFF signal is considered an approximate glottal source waveform [60,61], and the negative-to-positive zero-crossings (NPZCs) correspond to GCIs when considering the positive polarity of the signal [57,62]. The steps involved in the ZFF method are shown in Fig.…”

Section: Extraction Of Glottal Source Waveforms Using the Zff Methodsmentioning

confidence: 99%

Convolutional Neural Networks for Classification of Voice Qualities from Speech and Neck Surface Accelerometer Signals

Kadiri¹,

Javanmardi²,

Alku³

2022

Interspeech 2022

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Prior studies in the automatic classification of voice quality have mainly studied support vector machine (SVM) classifiers using the acoustic speech signal as input. Recently, one voice quality classification study was published using neck surface accelerometer (NSA) and speech signals as inputs and using SVMs with hand-crafted glottal source features. The present study examines simultaneously recorded NSA and speech signals in the classification of three voice qualities (breathy, modal, and pressed) using convolutional neural networks (CNNs) as classifier. The study has two goals: (1) to investigate which of the two signals (NSA vs. speech) is more useful in the classification task, and (2) to compare whether deep learning -based CNN classifiers with spectrogram and mel-spectrogram features are able to improve the classification accuracy compared to SVM classifiers using hand-crafted glottal source features. The results indicated that the NSA signal showed better classification of the voice qualities compared to the speech signal, and that the CNN classifier outperformed the SVM classifiers with large margins. The best mean classification accuracy was achieved with mel-spectrogram as input to the CNN classifier (93.8% for NSA and 90.6% for speech).

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“…Kadiri et al [34], Kadiri and Yegnanarayana [182], [183], and Kadiri and Alku [184] explored the features derived from the ZFF, ZTW, and SFF methods for discriminating phonation types. In these studies, cepstral coefficients were obtained from the spectra estimated by the three methods, and the cepstral coefficients were used in addition to excitation information scalar features [185], [186] (such as spectral statistics).…”

Section: A Study Of Phonation Typesmentioning

confidence: 99%

“…In these studies, cepstral coefficients were obtained from the spectra estimated by the three methods, and the cepstral coefficients were used in addition to excitation information scalar features [185], [186] (such as spectral statistics). Recently, in [184], the MFCCs computed from the glottal source waveforms estimated by the QCP method and the ZFF method were shown to be effective for the classification of different phonation types from speech signals.…”

Section: A Study Of Phonation Typesmentioning

confidence: 99%

Extraction and Utilization of Excitation Information of Speech: A Review

2021

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| Speech production can be regarded as a process where a time-varying vocal tract system (filter) is excited by a time-varying excitation. In addition to its linguistic message, the speech signal also carries information about, for example, the gender and age of the speaker. Moreover, the speech signal includes acoustical cues about several speaker traits, such as the emotional state and the state of health of the speaker. In order to understand the production of these acoustical cues by the human speech production mechanism and utilize this information in speech technology, it is necessary to extract features describing both the excitation and the filter of the human speech production mechanism. While the methods to estimate and parameterize the vocal tract system are well established, the excitation appears less studied. This article provides a review of signal processing approaches used for the extraction of excitation information from speech. This article highlights the importance of excitation information in the analysis and classification of phonation type and vocal emotions, in the analysis of nonverbal laughter sounds, and in studying pathological voices. Furthermore, recent developments of deep learning techniques in the context of extraction and utilization of the excitation information are discussed.

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Mel-Frequency Cepstral Coefficients of Voice Source Waveforms for Classification of Phonation Types in Speech

Cited by 12 publications

References 28 publications

Analysis and Detection of Pathological Voice Using Glottal Source Features

Analysis and Detection of Pathological Voice Using Glottal Source Features

Convolutional Neural Networks for Classification of Voice Qualities from Speech and Neck Surface Accelerometer Signals

Extraction and Utilization of Excitation Information of Speech: A Review

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