14thAnnual Conference of the International Speech Communication Association

Lyon, France
August 25-29, 2013

Stream Selection and Integration in Multistream ASR Using GMM-Based Performance Monitoring

Tetsuji Ogawa (1), Feipeng Li (2), Hynek Hermansky (2)

(1) Waseda University, Japan
(2) Johns Hopkins University, USA

A moderately deep and rather wide artificial neural net is applied in phoneme recognition of noisy speech. The net is formed by first estimating posterior probabilities of phonemes in 21 bandlimited streams covering the whole speech spectrum. These 21 band-limited streams are subdivided into three seven band-limited stream subsets, by differently sub-sampling the original 21 bandlimited streams. In the second processing stage, all non-empty combinations of seven band-limited streams from each subset are formed as inputs to 127 artificial neural nets that are again trained to yield phoneme posteriors. In this way, 127 ~ 3 = 381 processing streams are formed. A novel technique for finding the best combination of the resulting 381 parallel processing streams, which uses the likelihood of a single-state Gaussian mixture model of the final classifier output is applied to selecting the most efficient streams. The technique is efficient in phoneme recognition of speech that is corrupted by realistic additive noise.

Full Paper

Bibliographic reference.  Ogawa, Tetsuji / Li, Feipeng / Hermansky, Hynek (2013): "Stream selection and integration in multistream ASR using GMM-based performance monitoring", In INTERSPEECH-2013, 3332-3336.