A binaural scene analyzer for joint localization and recognition of speakers in the presence of interfering noise sources and reverberation

T. May, S.L.J.D.E. Par, van de, A.G. Kohlrausch

Research output: Contribution to journalArticleAcademicpeer-review

50 Citations (Scopus)
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Abstract

In this study, we present a binaural scene analyzer that is able to simultaneously localize, detect and identify a known number of target speakers in the presence of spatially positioned noise sources and reverberation. In contrast to many other binaural cocktail party processors, the proposed system does not require a priori knowledge about the azimuth position of the target speakers. The proposed system consists of three main building blocks: binaural localization, speech source detection, and automatic speaker identification. First, a binaural front-end is used to robustly localize relevant sound source activity. Second, a speech detection module based on missing data classification is employed to determine whether detected sound source activity corresponds to a speaker or to an interfering noise source using a binary mask that is based on spatial evidence supplied by the binaural front-end. Third, a second missing data classifier is used to recognize the speaker identities of all detected speech sources. The proposed system is systematically evaluated in simulated adverse acoustic scenarios. Compared to state-of-the art MFCC recognizers, the proposed model achieves significant speaker recognition accuracy improvements.
Original languageEnglish
Pages (from-to)2016-2030
Number of pages15
JournalIEEE Transactions on Audio, Speech, and Language Processing
Volume20
Issue number7
DOIs
Publication statusPublished - 2012

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