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Relevant bibliographies by topics / Narrowband Speech Spectrograms

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Journal articles
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Academic literature on the topic 'Narrowband Speech Spectrograms'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 September 2023

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Journal articles on the topic "Narrowband Speech Spectrograms"

1

Aragonda, Haricharan, and Chandra Sekhar Seelamantula. "Demodulation of Narrowband Speech Spectrograms Using the Riesz Transform." IEEE/ACM Transactions on Audio, Speech, and Language Processing 23, no. 11 (2015): 1824–34. http://dx.doi.org/10.1109/taslp.2015.2449088.

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2

Zhu, Shan. "Application of Ontology Matching Algorithm Based on Linguistic Features in English Pronunciation Quality Evaluation." Occupational Therapy International 2022 (June 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/2734672.

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Traditional English classroom teaching is difficult to meet the oral learning needs of most learners. Thanks to the continuous advancement of speech processing technology, computer-assisted language learning systems are becoming more intelligent, not only pointing out learners’ pronunciation errors but also assessing their overall pronunciation level. Method. This paper uses the method of tree kernel function to measure the similarity of two ontology trees. According to the features of nodes in ontology tree, methods to calculate the external features and internal features of nodes are propose

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3

Martínez-Arellano, Ana, Arantza Campo, Beatriz del Rio, Octavio Garaycochea, and Secundino Fernandez. "Describing the Acoustic and Vocal Production Characteristics of the Irrintzi: Feasibility of Its Use for the Treatment of Voice Disorders." Journal of Speech, Language, and Hearing Research, September 9, 2022, 1–9. http://dx.doi.org/10.1044/2022_jslhr-21-00394.

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Purpose: The aims of this study were to analyze and characterize the irrintzi (a folkloric shout emitted in a single breath used by the Basque people) acoustically and to describe the mechanism by which it is emitted. Method: Thirty-six sound samples of 12 female volunteers were analyzed. Acoustic analysis included primarily fundamental frequency ( f o ), spectral analysis in the time domain with a narrowband filter, and the use of linear predictive coding (LPC). Laryngeal and pharyngeal movements while emitting an irrintzi were studied by fiberoptic nasal endoscopy. Postural and mandible move

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4

Ikuma, Takeshi, Andrew J. McWhorter, Lacey Adkins, and Melda Kunduk. "Investigation of Vocal Bifurcations and Voice Patterns Induced by Asymmetry of Pathological Vocal Folds." Journal of Speech, Language, and Hearing Research, December 6, 2022, 1–13. http://dx.doi.org/10.1044/2022_jslhr-21-00499.

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Purpose: Vocal fold asymmetry creates irregular entrainments and modulations in voice, which may lead to rough perceptual quality. The presence of asymmetry can also cause mid-phonation bifurcations where a small change in the phonatory system causes a drastic change in vibration pattern, resulting in transitions in and out of rough voice. This study surveys sustained phonation recordings of speakers with the diagnoses of vocal fold polyp or unilateral vocal fold paralysis to investigate the resulting voice patterns. Method: This retrospective study observed 71 sustained phonation recordings f

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Dissertations / Theses on the topic "Narrowband Speech Spectrograms"

1

Aragonda, Haricharan. "Demodulation of Narrowband Speech Spectrograms." Thesis, 2014. http://etd.iisc.ac.in/handle/2005/2777.

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Speech is a non-stationary signal and contains modulations in both spectral and temporal domains. Based on the type of modulations studied, most speech processing algorithms can be classified into short-time analysis algorithms, narrow-band analysis algorithms, or joint spectro-temporal analysis algorithms. While traditional methods of speech analysis study the modulation along either time (Short-time analysis algorithms) or frequency (Narrowband analysis) at a time. A new class of algorithms that work simultaneously along both temporal as well as spectral dimensions, called the spectro-tempor

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2

Aragonda, Haricharan. "Demodulation of Narrowband Speech Spectrograms." Thesis, 2014. http://hdl.handle.net/2005/2777.

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Abstract:

Speech is a non-stationary signal and contains modulations in both spectral and temporal domains. Based on the type of modulations studied, most speech processing algorithms can be classified into short-time analysis algorithms, narrow-band analysis algorithms, or joint spectro-temporal analysis algorithms. While traditional methods of speech analysis study the modulation along either time (Short-time analysis algorithms) or frequency (Narrowband analysis) at a time. A new class of algorithms that work simultaneously along both temporal as well as spectral dimensions, called the spectro-tempor

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Conference papers on the topic "Narrowband Speech Spectrograms"

1

Aragonda, Haricharan, and Chandra Sekhar Seelamantula. "Riesz-transform-based demodulation of narrowband spectrograms of voiced speech." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6639264.

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2

Cheung, S., and J. S. Lim. "Combined multi-resolution (wideband/narrowband) spectrogram." In [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1991. http://dx.doi.org/10.1109/icassp.1991.150375.

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