Wang et al., 2021 - Google Patents

TSTNN: Two-stage transformer based neural network for speech enhancement in the time domain

Wang et al., 2021

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Document ID
938644009392487728
Author
Wang K
He B
Zhu W
Publication year
Publication venue
ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we propose a transformer-based architecture, called two-stage transformer neural network (TSTNN) for end-to-end speech denoising in the time domain. The proposed model is composed of an encoder, a two-stage transformer module (TSTM), a masking …
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Classifications

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    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
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    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/14—Speech classification or search using statistical models, e.g. hidden Markov models [HMMs]
    • G10L15/142—Hidden Markov Models [HMMs]
    • G10L15/144—Training of HMMs
    • G—PHYSICS
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    • G10L25/18—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
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    • G10L15/06—Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
    • G10L15/065—Adaptation
    • G10L15/07—Adaptation to the speaker
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    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
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    • G—PHYSICS
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    • G10L17/00—Speaker identification or verification
    • G10L17/04—Training, enrolment or model building

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