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Electrical Engineering and Systems Science > Audio and Speech Processing

arXiv:2211.00484 (eess)
[Submitted on 31 Oct 2022]

Title:Fast and parallel decoding for transducer

Authors:Wei Kang, Liyong Guo, Fangjun Kuang, Long Lin, Mingshuang Luo, Zengwei Yao, Xiaoyu Yang, Piotr Żelasko, Daniel Povey
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Abstract:The transducer architecture is becoming increasingly popular in the field of speech recognition, because it is naturally streaming as well as high in accuracy. One of the drawbacks of transducer is that it is difficult to decode in a fast and parallel way due to an unconstrained number of symbols that can be emitted per time step. In this work, we introduce a constrained version of transducer loss to learn strictly monotonic alignments between the sequences; we also improve the standard greedy search and beam search algorithms by limiting the number of symbols that can be emitted per time step in transducer decoding, making it more efficient to decode in parallel with batches. Furthermore, we propose an finite state automaton-based (FSA) parallel beam search algorithm that can run with graphs on GPU efficiently. The experiment results show that we achieve slight word error rate (WER) improvement as well as significant speedup in decoding. Our work is open-sourced and publicly available\footnote{this https URL}.
Comments: Submitted to 2023 IEEE International Conference on Acoustics, Speech and Signal Processing
Subjects: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD)
Cite as: arXiv:2211.00484 [eess.AS]
  (or arXiv:2211.00484v1 [eess.AS] for this version)
  https://doi.org/10.48550/arXiv.2211.00484

Submission history

From: Daniel Povey [view email]
[v1] Mon, 31 Oct 2022 07:46:10 UTC (38 KB)
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