Improving endpoint detection in end-to-end streaming ASR for conversational speech
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913854639833088 |
|---|---|
| author | C, Anandh Durai, Karthik Pandia Prakash, Jeena Arumugam, Manickavela Hacioglu, Kadri Dubagunta, S. Pavankumar Stolcke, Andreas Venkatesan, Shankar Ganapathiraju, Aravind |
| author_facet | C, Anandh Durai, Karthik Pandia Prakash, Jeena Arumugam, Manickavela Hacioglu, Kadri Dubagunta, S. Pavankumar Stolcke, Andreas Venkatesan, Shankar Ganapathiraju, Aravind |
| contents | ASR endpointing (EP) plays a major role in delivering a good user experience in products supporting human or artificial agents in human-human/machine conversations. Transducer-based ASR (T-ASR) is an end-to-end (E2E) ASR modelling technique preferred for streaming. A major limitation of T-ASR is delayed emission of ASR outputs, which could lead to errors or delays in EP. Inaccurate EP will cut the user off while speaking, returning incomplete transcript while delays in EP will increase the perceived latency, degrading the user experience. We propose methods to improve EP by addressing delayed emission along with EP mistakes. To address the delayed emission problem, we introduce an end-of-word token at the end of each word, along with a delay penalty. The EP delay is addressed by obtaining a reliable frame-level speech activity detection using an auxiliary network. We apply the proposed methods on Switchboard conversational speech corpus and evaluate it against a delay penalty method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17070 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving endpoint detection in end-to-end streaming ASR for conversational speech C, Anandh Durai, Karthik Pandia Prakash, Jeena Arumugam, Manickavela Hacioglu, Kadri Dubagunta, S. Pavankumar Stolcke, Andreas Venkatesan, Shankar Ganapathiraju, Aravind Computation and Language Artificial Intelligence Sound Audio and Speech Processing ASR endpointing (EP) plays a major role in delivering a good user experience in products supporting human or artificial agents in human-human/machine conversations. Transducer-based ASR (T-ASR) is an end-to-end (E2E) ASR modelling technique preferred for streaming. A major limitation of T-ASR is delayed emission of ASR outputs, which could lead to errors or delays in EP. Inaccurate EP will cut the user off while speaking, returning incomplete transcript while delays in EP will increase the perceived latency, degrading the user experience. We propose methods to improve EP by addressing delayed emission along with EP mistakes. To address the delayed emission problem, we introduce an end-of-word token at the end of each word, along with a delay penalty. The EP delay is addressed by obtaining a reliable frame-level speech activity detection using an auxiliary network. We apply the proposed methods on Switchboard conversational speech corpus and evaluate it against a delay penalty method. |
| title | Improving endpoint detection in end-to-end streaming ASR for conversational speech |
| topic | Computation and Language Artificial Intelligence Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2505.17070 |