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- W4375868938 abstract "This paper presents a novel streaming automatic speech recognition (ASR) framework for multi-talker overlapping speech captured by a distant microphone array with an arbitrary geometry. Our framework, named t-SOT-VA, capitalizes on independently developed two recent technologies; array-geometry-agnostic continuous speech separation, or VarArray, and streaming multi-talker ASR based on token-level serialized output training (t-SOT). To combine the best of both technologies, we newly design a t-SOT-based ASR model that generates a serialized multi-talker transcription based on two separated speech signals from VarArray. We also propose a pre-training scheme for such an ASR model where we simulate VarArray’s output signals based on monaural single-talker ASR training data. Conversation transcription experiments using the AMI meeting corpus show that the system based on the proposed framework significantly outperforms conventional ones. Our system achieves the state-of-the-art word error rates of 13.7% and 15.5% for the AMI development and evaluation sets, respectively, in the multiple-distant-microphone setting while retaining the streaming inference capability." @default.
- W4375868938 created "2023-05-10" @default.
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- W4375868938 date "2023-06-04" @default.
- W4375868938 modified "2023-10-16" @default.
- W4375868938 title "Vararray Meets T-Sot: Advancing the State of the Art of Streaming Distant Conversational Speech Recognition" @default.
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- W4375868938 doi "https://doi.org/10.1109/icassp49357.2023.10095367" @default.
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