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Speech recognition by man and machine: Influence of speaking rate, style, and effort on the recognition performance of human listeners and automatic classifiers - Tapa blanda
Sinopsis
While human listeners have little problems in dealing with the strong variation in spoken language, the same cannot be said about automatic speech recognition (ASR). This work compares recognition performance of man and machine with the aim of learning from the distinct errors between these two. Based on the differences, the signal processing mechanisms are analyzed that are suitable to increase the robustness of ASR. The comparison focuses on the influence of intrinsic variation of speech, i.e., changes in speaking rate, effort and style, as well as dialect and accent. The outcome of the experiments suggests that the processing of temporal cues in ASR bears room for improvement. Therefore, spectro-temporal features are employed as input to ASR systems, which results in an increase of recognition performance for varying speaking effort and speaking style compared to standard features. This documents the usefulness of spectro-temporal and temporal information for automatic recognizers.
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Reseña del editor
While human listeners have little problems in dealing with the strong variation in spoken language, the same cannot be said about automatic speech recognition (ASR). This work compares recognition performance of man and machine with the aim of learning from the distinct errors between these two. Based on the differences, the signal processing mechanisms are analyzed that are suitable to increase the robustness of ASR. The comparison focuses on the influence of intrinsic variation of speech, i.e., changes in speaking rate, effort and style, as well as dialect and accent. The outcome of the experiments suggests that the processing of temporal cues in ASR bears room for improvement. Therefore, spectro-temporal features are employed as input to ASR systems, which results in an increase of recognition performance for varying speaking effort and speaking style compared to standard features. This documents the usefulness of spectro-temporal and temporal information for automatic recognizers.
Biografía del autor
Bernd T. Meyer studied physics at the University in Oldenburg, and received his diploma and Ph.D. in 2004 and 2009, respectively. He has been working on the improvement of automatic speech recognizers and modeling human speech perception both in Oldenburg and the International Computer Science Institute in Berkeley, CA.
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Speech recognition by man and machine: Influence of speaking rate, style, and effort on the recognition performance of human listeners and automatic classifiers
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Suedwestdeutscher Verlag fuer Hochschulschriften, 2010
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Speech recognition by man and machine: Influence of speaking rate, style, and effort on the recognition performance of human listeners and automatic classifiers
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Suedwestdeutscher Verlag fuer Hochschulschriften, 2010
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ISBN 13: 9783838121550
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Speech recognition by man and machine Influence of speaking rate, style, and effort on the recognition performance of human listeners and automatic classifiers
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Suedwestdeutscher Verlag fuer Hochschulschriften, 2010
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ISBN 13: 9783838121550
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Speech recognition by man and machine: Influence of speaking rate, style, and effort on the recognition performance of human listeners and automatic classifiers
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Suedwestdeutscher Verlag fuer Hochschulschriften, 2010
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ISBN 13: 9783838121550
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Speech recognition by man and machine Influence of speaking rate, style, and effort on the recognition performance of human listeners and automatic classifiers
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Suedwestdeutscher Verlag fuer Hochschulschriften, 2010
ISBN 10: 3838121554
ISBN 13: 9783838121550
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Speech recognition by man and machine: Influence of speaking rate, style, and effort on the recognition performance of human listeners and automatic classifiers
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Suedwestdeutscher Verlag fuer Hochschulschriften, 2010
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Speech recognition by man and machine: Influence of speaking rate, style, and effort on the recognition performance of human listeners and automatic classifiers
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Speech recognition by man and machine
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Südwestdeutscher Verlag für Hochschulschriften, 2010
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Kartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. While human listeners have little problems in dealing with the strong variation in spoken language, the same cannot be said about automatic speech recognition (ASR). This work compares recognition performance of man and machine with the aim of learning from. Nº de ref. del artículo: 5406503
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Speech recognition by man and machine : Influence of speaking rate, style, and effort on the recognition performance of human listeners and automatic classifiers
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - While human listeners have little problems in dealing with the strong variation in spoken language, the same cannot be said about automatic speech recognition (ASR). This work compares recognition performance of man and machine with the aim of learning from the distinct errors between these two. Based on the differences, the signal processing mechanisms are analyzed that are suitable to increase the robustness of ASR. The comparison focuses on the influence of intrinsic variation of speech, i.e., changes in speaking rate, effort and style, as well as dialect and accent. The outcome of the experiments suggests that the processing of temporal cues in ASR bears room for improvement. Therefore, spectro-temporal features are employed as input to ASR systems, which results in an increase of recognition performance for varying speaking effort and speaking style compared to standard features. This documents the usefulness of spectro-temporal and temporal information for automatic recognizers. Nº de ref. del artículo: 9783838121550
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Speech Recognition by Man and Machine
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
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