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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.00295 |
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| _version_ | 1866913715978240000 |
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| author | Yang, Qiaoyu |
| author_facet | Yang, Qiaoyu |
| contents | The expanding feature set of modern headphones puts a challenge on the design of their control interface. Users may want to separately control each feature or quickly switch between modes that activate different features. Traditional approach of physical buttons may no longer be feasible when the feature set is large. Keyword spotting with voice commands is a promising solution to the issue. Most existing methods of keyword spotting only support commands spoken in a regular voice. However, regular voice may not be desirable in quiet places or public settings. In this paper, we investigate the problem of on-device keyword spotting in whisper voice and explore approaches to improve noise robustness. We leverage the inner microphone on noise-cancellation headphones as an additional source of voice input. We also design a curriculum learning strategy that gradually increases the proportion of whisper keywords during training. We demonstrate through experiments that the combination of multi-microphone processing and curriculum learning could improve F1 score of whisper keyword spotting by up to 15% in noisy conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00295 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Toward noise-robust whisper keyword spotting on headphones with in-earcup microphone and curriculum learning Yang, Qiaoyu Audio and Speech Processing Sound The expanding feature set of modern headphones puts a challenge on the design of their control interface. Users may want to separately control each feature or quickly switch between modes that activate different features. Traditional approach of physical buttons may no longer be feasible when the feature set is large. Keyword spotting with voice commands is a promising solution to the issue. Most existing methods of keyword spotting only support commands spoken in a regular voice. However, regular voice may not be desirable in quiet places or public settings. In this paper, we investigate the problem of on-device keyword spotting in whisper voice and explore approaches to improve noise robustness. We leverage the inner microphone on noise-cancellation headphones as an additional source of voice input. We also design a curriculum learning strategy that gradually increases the proportion of whisper keywords during training. We demonstrate through experiments that the combination of multi-microphone processing and curriculum learning could improve F1 score of whisper keyword spotting by up to 15% in noisy conditions. |
| title | Toward noise-robust whisper keyword spotting on headphones with in-earcup microphone and curriculum learning |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2502.00295 |