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arXiv · 2306.03778

Streaming Speech-to-Confusion Network Speech Recognition

Denis Filimonov·Prabhat Pandey·Ariya Rastrow·Ankur Gandhe·Andreas Stolcke
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Abstract

In interactive automatic speech recognition (ASR) systems, low-latency requirements limit the amount of search space that can be explored during decoding, particularly in end-to-end neural ASR. In this paper, we present a novel streaming ASR architecture that outputs a confusion network while maintaining limited latency, as needed for interactive applications. We show that 1-best results of our model are on par with a comparable RNN-T system, while the richer hypothesis set allows second-pass rescoring to achieve 10-20\% lower word error rate on the LibriSpeech task. We also show that our model outperforms a strong RNN-T baseline on a far-field voice assistant task.

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Denis Filimonov, Prabhat Pandey, Ariya Rastrow, Ankur Gandhe, Andreas Stolcke. 2023-06-02. Streaming Speech-to-Confusion Network Speech Recognition. https://doi.org/10.21437/interspeech.2023-486

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