Comparison of Glottal Closure Instants Detection Algorithms for Emotional Speech

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

doi:10.1109/icassp40776.2020.9054737

Cited by 9 publications

(2 citation statements)

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“…The cost function consists of various elements, such as the interpulse similarity, normalized energy values, pitch deviation, costs derived from the projected phase slope, and deviations from an ideal phase slope function. More details on the GCI detection methods and the GCI-based analysis of speech processing can be found in [28], [29], [129], [146], and [148].…”

Section: ) Methods Based On Processing the Excitation Signalmentioning

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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Section: ) Methods Based On Processing the Excitation Signalmentioning

confidence: 99%

Extraction and Utilization of Excitation Information of Speech: A Review

2021

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“…The studies in [13,14], show that the performance of state-ofthe-art epoch extraction methods is efficient in clean speech conditions. Efficacy of epoch extraction methods has been studied for telephonic quality speech [15,16,17], emotional speech [18,19], and the degraded speech obtained by corrupting the clean speech with additive noise and reverberations [20,21]. In general, the performance of these methods has been evaluated using speech utterances produced by healthy (controlled) speakers.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Different Epoch Extraction Methods for Speech Associated with Voice Disorders

Barche

Gurugubelli

Vuppala

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Accurate detection of epoch locations is important in extracting the features from the speech signal for automatic detection and assessment of voice disorders. Therefore, this study aimed to compare the various algorithms for detecting epoch locations from the speech associated with voice disorders. In this regard, nine state-of-theart epoch extraction algorithms were considered, and their performance for different categories of voice disorders was evaluated on the SVD dataset. Experimental results indicate that most of the epoch extraction methods showed better performance for healthy speech; however, their performance was degraded for speech associated with voice disorders. Furthermore, the performance of epoch extraction methods was degraded for the speech of structural and neurogenic disorders compared to the speech of psychogenic and functional disorders. Among the different epoch extraction algorithms, zero phase-zero frequency filtering showed the best performance in terms identification rate (90.37%) and identification accuracy (0.34ms), for speech associated with voice disorders.

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