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International Journal of Speech Technology
Erschienen in: International Journal of Speech Technology 4/2016

04.08.2016

Stream fusion for multi-stream automatic speech recognition

verfasst von: Hesam Sagha, Feipeng Li, Ehsan Variani, José del R. Millán, Ricardo Chavarriaga, Björn Schuller

Erschienen in: International Journal of Speech Technology | Ausgabe 4/2016

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Abstract

Multi-stream automatic speech recognition (MS-ASR) has been confirmed to boost the recognition performance in noisy conditions. In this system, the generation and the fusion of the streams are the essential parts and need to be designed in such a way to reduce the effect of noise on the final decision. This paper shows how to improve the performance of the MS-ASR by targeting two questions; (1) How many streams are to be combined, and (2) how to combine them. First, we propose a novel approach based on stream reliability to select the number of streams to be fused. Second, a fusion method based on Parallel Hidden Markov Models is introduced. Applying the method on two datasets (TIMIT and RATS) with different noises, we show an improvement of MS-ASR.
Vorheriger Artikel An optimal two stage feature selection for speech emotion recognition using acoustic features
Nächster Artikel A wavelet- based transform method for quality improvement in noisy speech patterns of Arabic language

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Fußnoten
1
We used the Quicknet toolbox developed at the International Computer Science Institute (http://​www1.​icsi.​berkeley.​edu/​Speech/​qn.​html).
 
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Metadaten
Titel
Stream fusion for multi-stream automatic speech recognition
verfasst von
Hesam Sagha
Feipeng Li
Ehsan Variani
José del R. Millán
Ricardo Chavarriaga
Björn Schuller
Publikationsdatum
04.08.2016
Verlag
Springer US
Erschienen in
International Journal of Speech Technology / Ausgabe 4/2016
Print ISSN: 1381-2416
Elektronische ISSN: 1572-8110
DOI
https://doi.org/10.1007/s10772-016-9357-1

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