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Using Automatic Speech Recognition to Assess Thai Speech Language Fluency in the Montreal Cognitive Assessment (MoCA)
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In: Sensors; Volume 22; Issue 4; Pages: 1583 (2022)
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Utterance partitioning for speaker recognition: an experimental review and analysis with new findings under GMM-SVM framework
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In: ISSN: 1381-2416 ; EISSN: 1572-8110 ; International Journal of Speech Technology ; https://hal.archives-ouvertes.fr/hal-03232723 ; International Journal of Speech Technology, Springer Verlag, In press, ⟨10.1007/s10772-021-09862-8⟩ (2021)
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Abstract:
International audience ; The performance of speaker recognition system is highly dependent on the amount of speech used in enrollment and test. This work presents a detailed experimental review and analysis of the GMM-SVM based speaker recognition system in presence of duration variability. This article also reports a comparison of the performance of GMM-SVM classifier with its precursor technique Gaussian mixture model-universal background model (GMM-UBM) classifier in presence of duration variability. The goal of this research work is not to propose a new algorithm for improving speaker recognition performance in presence of duration variability. However, the main focus of this work is on utterance partitioning (UP), a commonly used strategy to compensate the duration variability issue. We have analysed in detailed the impact of training utterance partitioning in speaker recognition performance under GMM-SVM framework. We further investigate the reason why the utterance partitioning is important for boosting speaker recognition performance. We have also shown in which case the utterance partitioning could be useful and where not. Our study has revealed that utterance partitioning does not reduce the data imbalance problem of the GMM-SVM classifier as claimed in earlier study. Apart from these, we also discuss issues related to the impact of parameters such as number of Gaussians, supervector length, amount of splitting required for obtaining better performance in short and long duration test conditions from speech duration perspective. We have performed the experiments with telephone speech from POLYCOST corpus consisting of 130 speakers.
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Keyword:
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing; Duration Variability; GMM-SVM Classifier; GMM-UMB Classifier; Short Test Utterance; Speaker Recognition; Utterance Partitioning
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URL: https://hal.archives-ouvertes.fr/hal-03232723/file/Manuscript_UtterancePartition.pdf https://hal.archives-ouvertes.fr/hal-03232723/document https://hal.archives-ouvertes.fr/hal-03232723 https://doi.org/10.1007/s10772-021-09862-8
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Evaluation eines russischen Sprachverständlichkeitstests ... : Evaluation of a Russian Speech Intelligibility Test ...
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The Author Naming Task: A newly-proposed measure for assessing print exposure ...
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Objective and behavioural measures of temporal processing in cochlear implant users. ...
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Shared Storybook Reading and the Development of Oral Language Skills ... : Das gemeinsame Lesen von Kinderbüchern und die Entwicklung frühkindlicher Sprachfähigkeiten ...
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Whole Word vs Phoneme Error Scoring for Audiological Word Recognition Testing
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In: Honors Theses (2020)
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Eye see through you! Eye tracking unmasks concealed face recognition despite countermeasures
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Text extraction in document images: highlight on using corner points
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In: Proceedings of 12th International Workshop on Document Analysis Systems ; International Workshop on Document Analysis Systems (DAS) ; https://hal.archives-ouvertes.fr/hal-01269802 ; International Workshop on Document Analysis Systems (DAS), Apr 2016, Santorini, Greece (2016)
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Effect of Speech Recognition Testing on Self-Reported State Anxiety
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Code-switched English Pronunciation Modeling for Swahili Spoken Term Detection (Pub Version, Open Access)
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An end-to-end approach to language identification in short utterances using convolutional neural networks
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Pulse-spreading harmonic complex as an alternative carrier for vocoder simulations of cochlear implants.
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In: J Acoust Soc Am , 139 (2) pp. 986-991. (2016) (2016)
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