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Speech segregation based on sound localization.

Nicoleta Roman·DeLiang Wang·Guy J Brown
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Abstract

At a cocktail party, one can selectively attend to a single voice and filter out all the other acoustical interferences. How to simulate this perceptual ability remains a great challenge. This paper describes a novel, supervised learning approach to speech segregation, in which a target speech signal is separated from interfering sounds using spatial localization cues: interaural time differences (ITD) and interaural intensity differences (IID). Motivated by the auditory masking effect, the notion of an "ideal" time-frequency binary mask is suggested, which selects the target if it is stronger than the interference in a local time-frequency (T-F) unit. It is observed that within a narrow frequency band, modifications to the relative strength of the target source with respect to the interference trigger systematic changes for estimated ITD and IID. For a given spatial configuration, this interaction produces characteristic clustering in the binaural feature space. Consequently, pattern classification is performed in order to estimate ideal binary masks. A systematic evaluation in terms of signal-to-noise ratio as well as automatic speech recognition performance shows that the resulting system produces masks very close to ideal binary ones. A quantitative comparison shows that the model yields significant improvement in performance over an existing approach. Furthermore, under certain conditions the model produces large speech intelligibility improvements with normal listeners.

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AdultAttentionDichotic Listening TestsFemaleHumansMaleMathematical ComputingPerceptual MaskingSound LocalizationSound SpectrographySpeech AcousticsSpeech IntelligibilitySpeech PerceptionJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, Non-P.H.S.

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Nicoleta Roman, DeLiang Wang, Guy J Brown. 2003. Speech segregation based on sound localization.. https://doi.org/10.1121/1.1610463

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