| Autor: |
Behre, Piyush, Tan, Sharman, Varadharajan, Padma, Chang, Shuangyu |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
8th International Conference on Signal, Image Processing and Embedded Systems (SIGEM 2022), Volume 12, Number 20, November 2022 |
| Druh dokumentu: |
Working Paper |
| Popis: |
While speech recognition Word Error Rate (WER) has reached human parity for English, long-form dictation scenarios still suffer from segmentation and punctuation problems resulting from irregular pausing patterns or slow speakers. Transformer sequence tagging models are effective at capturing long bi-directional context, which is crucial for automatic punctuation. Automatic Speech Recognition (ASR) production systems, however, are constrained by real-time requirements, making it hard to incorporate the right context when making punctuation decisions. In this paper, we propose a streaming approach for punctuation or re-punctuation of ASR output using dynamic decoding windows and measure its impact on punctuation and segmentation accuracy across scenarios. The new system tackles over-segmentation issues, improving segmentation F0.5-score by 13.9%. Streaming punctuation achieves an average BLEU-score improvement of 0.66 for the downstream task of Machine Translation (MT). |
| Databáze: |
arXiv |
| Externí odkaz: |
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