ArticleThe Journal of the Acoustical Society of America2020
A two-stage deep learning algorithm for talker-independent speaker separation in reverberant conditions.
Article in The Journal of the Acoustical Society of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 5 citations in OpenAlex.
- A two-stage deep learning algorithm for talker-independent speaker separation in reverberant conditions.The Journal of the Acoustical Society of America · 2020Article
- Causal Deep CASA for Monaural Talker-Independent Speaker Separation.IEEE/ACM transactions on audio, speech, and language processing · 2020Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Speaker separation is a special case of speech separation, in which the mixture signal comprises two or more speakers. Many talker-independent speaker separation methods have been introduced in recent years to address this problem in anechoic conditions. To consider more realistic environments, this paper investigates talker-independent speaker separation in reverberant conditions. To effectively deal with speaker separation and speech dereverberation, extending the deep computational auditory scene analysis (CASA) approach to a two-stage system is proposed. In this method, reverberant utterances are first separated and separated utterances are then dereverberated. The proposed two-stage deep CASA system significantly outperforms a baseline one-stage deep CASA method in real reverberant conditions. The proposed system has superior separation performance at the frame level and higher accuracy in assigning separated frames to individual speakers. The proposed system successfully generalizes to an unseen speech corpus and exhibits similar performance to a talker-dependent system.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.