Invention Grant
- Patent Title: Speech enhancement machine learning model for estimation of reverberation in a multi-task learning framework
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Application No.: US16988423Application Date: 2020-08-07
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Publication No.: US12014748B1Publication Date: 2024-06-18
- Inventor: Ritwik Giri , Mehmet Umut Isik , Neerad Dilip Phansalkar , Jean-Marc Valin , Karim Helwani , Arvindh Krishnaswamy
- Applicant: Amazon Technologies, Inc.
- Applicant Address: US WA Seattle
- Assignee: Amazon Technologies, Inc.
- Current Assignee: Amazon Technologies, Inc.
- Current Assignee Address: US WA Seattle
- Agency: NICHOLSON DE VOS WEBSTER & ELLIOTT LLP
- Main IPC: G10L21/0208
- IPC: G10L21/0208 ; G06N5/04 ; G06N20/00 ; G10L21/034

Abstract:
Techniques for training and using a machine learning model for estimation of reverberation in a multi-task learning framework are described. According to some embodiments, the multi-task learning framework improves the performance of the machine learning model by estimating the amount of reverberation present in an input audio recording as a secondary task to the primary task of generating a clean speech portion of the input audio recording. In one embodiment, a model architecture is selected that takes a noisy reverberant recording as an input and outputs an estimate of a clean (e.g., de-reverberated) signal, an estimate of noise (e.g., background noise), and an estimate of the reverb only portion, with the secondary task of estimating the reverb only portion acting as a regularizer that improves the machine learning model's performance in enhancing the reverberant (e.g., and noisy) input speech.
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