Dataset Information

Causal Deep CASA for Monaural Talker-Independent Speaker Separation.

ABSTRACT: Talker-independent monaural speaker separation aims to separate concurrent speakers from a single-microphone recording. Inspired by human auditory scene analysis (ASA) mechanisms, a two-stage deep CASA approach has been proposed recently to address this problem, which achieves state-of-the-art results in separating mixtures of two or three speakers. A main limitation of deep CASA is that it is a non-causal system, while many speech processing applications, e.g., telecommunication and hearing prosthesis, require causal processing. In this study, we propose a causal version of deep CASA to address this limitation. First, we modify temporal connections, normalization and clustering algorithms in deep CASA so that no future information is used throughout the deep network. We then train a C-speaker (C ≥ 2) deep CASA system in a speaker-number-independent fashion, generalizable to speech mixtures with up to C speakers without the prior knowledge about the speaker number. Experimental results show that causal deep CASA achieves excellent speaker separation performance with known or unknown speaker numbers.

SUBMITTER: Liu Y

PROVIDER: S-EPMC7654633 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Causal Deep CASA for Monaural Talker-Independent Speaker Separation.

Liu Yuzhou Y Wang DeLiang D

IEEE/ACM transactions on audio, speech, and language processing 20200708

Talker-independent monaural speaker separation aims to separate concurrent speakers from a single-microphone recording. Inspired by human auditory scene analysis (ASA) mechanisms, a two-stage deep CASA approach has been proposed recently to address this problem, which achieves state-of-the-art results in separating mixtures of two or three speakers. A main limitation of deep CASA is that it is a non-causal system, while many speech processing applications, e.g., telecommunication and hearing pro ...[more]

PMID: 33178880

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Project description:ObjectiveSome cochlear implant (CI) users report having difficulty accessing indexical information in the speech signal, presumably due to limitations in the transmission of fine spectrotemporal cues. The purpose of this review article was to systematically review and evaluate the existing research on talker processing in CI users. Specifically, we reviewed the performance of CI users in three types of talker- and voice-related tasks. We also examined the different factors (such as participant, hearing, and device characteristics) that might influence performance in these specific tasks.DesignWe completed a systematic search of the literature with select key words using citation aggregation software to search Google Scholar. We included primary reports that tested (a) talker discrimination, (b) voice perception, and (c) talker identification. Each report must have had at least one group of participants with CIs. Each included study was also evaluated for quality of evidence.ResultsThe searches resulted in 1,561 references, which were first screened for inclusion and then evaluated in full. Forty-three studies examining talker discrimination, voice perception, and talker identification were included in the final review. Most studies were focused on postlingually deafened and implanted adult CI users, with fewer studies focused on prelingual implant users. In general, CI users performed above chance in these tasks. When there was a difference between groups, CI users performed less accurately than their normal-hearing (NH) peers. A subset of CI users reached the same level of performance as NH participants exposed to noise-vocoded stimuli. Some studies found that CI users and NH participants relied on different cues for talker perception. Within groups of CI users, there is moderate evidence for a bimodal benefit for talker processing, and there are mixed findings about the effects of hearing experience.ConclusionsThe current review highlights the challenges faced by CI users in tracking and recognizing voices and how they adapt to it. Although large variability exists, there is evidence that CI users can process indexical information from speech, though with less accuracy than their NH peers. Recent work has described some of the factors that might ease the challenges of talker processing in CI users. We conclude by suggesting some future avenues of research to optimize real-world speech outcomes.

Dataset Information

Causal Deep CASA for Monaural Talker-Independent Speaker Separation.

Publications

Causal Deep CASA for Monaural Talker-Independent Speaker Separation.

Similar Datasets

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