Analysis and classification of phonation types in speech and singing voice

Kadiri, Sudarsana Reddy; Alku, Paavo; Yegnanarayana, B.

doi:10.1016/j.specom.2020.02.004

Cited by 20 publications

(18 citation statements)

References 58 publications

(112 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the future, we plan to explore the Mel-SFF spectrogram derived features for dialect identification in noisy conditions [27], [28], [30] and for larger corpora. Further, we plan to investigate the complementary information between the SFF based spectrograms and zero-time windowing (ZTW) based spectrograms, which were shown to give better performance over STFT [42]- [44].…”

Section: Discussionmentioning

confidence: 99%

Mel-Weighted Single Frequency Filtering Spectrogram for Dialect Identification

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Mel-Weighted Single Frequency Filtering Spectrogram for Dialect Identification

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…This impulse-like component is caused by the sudden deceleration of the air flow in the vicinity of the GCI due to adduction of the vocal folds. The characteristics of this impulse-like event in the excitation waveform, particularly its sharpness, are closely associated with several important speech attributes, such as voice quality [11], [34] and loudness [35], [36]. The excitation information also includes identifying the locations of GOIs, where secondary excitation of the vocal tract might take place.…”

Section: Demonstration Of the Production Of Voiced Speech In Two Phonation Types: Normal (Left) And Breathy (Right) The Upper Part Of Thementioning

confidence: 99%

“…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 that study, the glottal source features were derived from the ZFF signal and the glottal flow waveform estimated using the QCP method [59]. The features derived from the ZFF signal consisted of the SoE, EoE, loudness measure, and ZFF signal energy [34], [181], [182]. The glottal flow signals estimated using QCP were parameterized in terms of time-and frequency-domain glottal features [30], [257].…”

Section: Study Of Pathological Voicesmentioning

confidence: 99%

See 1 more Smart Citation

Extraction and Utilization of Excitation Information of Speech: A Review

2021

Self Cite

View full text Add to dashboard Cite

| 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.

show abstract

“…Glottal source features were used with the mel-frequency cepstral coefficients (MFCCs) in [18,23] for the automatic classification of voice qualities from speech signals. The experiments reported in [31,32] used features derived from the approximate glottal source signals such as the linear prediction residual and the zero frequency filtered signal (ZFF signal) to characterize voice qualities in speech and singing. Features including the energy of the ZFF signal, the slope of the ZFF signal at zero crossings, the energy of excitation, and the loudness measure were used for analysis and automatic classification of voice qualities in [31,32].…”

Section: Introductionmentioning

confidence: 99%

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

Kadiri¹,

Javanmardi²,

Alku³

2022

Interspeech 2022

Self Cite

View full text Add to dashboard Cite

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).

show abstract

Analysis and classification of phonation types in speech and singing voice

Cited by 20 publications

References 58 publications

Mel-Weighted Single Frequency Filtering Spectrogram for Dialect Identification

Mel-Weighted Single Frequency Filtering Spectrogram for Dialect Identification

Extraction and Utilization of Excitation Information of Speech: A Review

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

Contact Info

Product

Resources

About