Tiny Transducer: A Highly-Efficient Speech Recognition Model on Edge Devices
| Autor: | Long Ma, Yuekai Zhang, Sining Sun |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Sound (cs.SD) Signal processing Edge device Computer science Feed forward Blank Computer Science - Sound Transducer Audio and Speech Processing (eess.AS) FOS: Electrical engineering electronic engineering information engineering Graph (abstract data type) Algorithm Encoder Decoding methods Electrical Engineering and Systems Science - Audio and Speech Processing |
| Zdroj: | ICASSP |
| DOI: | 10.1109/icassp39728.2021.9413854 |
| Popis: | This paper proposes an extremely lightweight phone-based transducer model with a tiny decoding graph on edge devices. First, a phone synchronous decoding (PSD) algorithm based on blank label skipping is first used to speed up the transducer decoding process. Then, to decrease the deletion errors introduced by the high blank score, a blank label deweighting approach is proposed. To reduce parameters and computation, deep feedforward sequential memory network (DFSMN) layers are used in the transducer encoder, and a CNN-based stateless predictor is adopted. SVD technology compresses the model further. WFST-based decoding graph takes the context-independent (CI) phone posteriors as input and allows us to flexibly bias user-specific information. Finally, with only 0.9M parameters after SVD, our system could give a relative 9.1% - 20.5% improvement compared with a bigger conventional hybrid system on edge devices. Comment: Accepted by ICASSP 2021 |
| Databáze: | OpenAIRE |
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