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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2407.06078 |
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| _version_ | 1866916315889926144 |
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| author | Yuan, Junming Shi, Ying Li, LanTian Wang, Dong Hamdulla, Askar |
| author_facet | Yuan, Junming Shi, Ying Li, LanTian Wang, Dong Hamdulla, Askar |
| contents | Few-shot keyword spotting (KWS) aims to detect unknown keywords with limited training samples. A commonly used approach is the pre-training and fine-tuning framework. While effective in clean conditions, this approach struggles with mixed keyword spotting -- simultaneously detecting multiple keywords blended in an utterance, which is crucial in real-world applications. Previous research has proposed a Mix-Training (MT) approach to solve the problem, however, it has never been tested in the few-shot scenario. In this paper, we investigate the possibility of using MT and other relevant methods to solve the two practical challenges together: few-shot and mixed speech. Experiments conducted on the LibriSpeech and Google Speech Command corpora demonstrate that MT is highly effective on this task when employed in either the pre-training phase or the fine-tuning phase. Moreover, combining SSL-based large-scale pre-training (HuBert) and MT fine-tuning yields very strong results in all the test conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_06078 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Few-Shot Keyword Spotting from Mixed Speech Yuan, Junming Shi, Ying Li, LanTian Wang, Dong Hamdulla, Askar Sound Few-shot keyword spotting (KWS) aims to detect unknown keywords with limited training samples. A commonly used approach is the pre-training and fine-tuning framework. While effective in clean conditions, this approach struggles with mixed keyword spotting -- simultaneously detecting multiple keywords blended in an utterance, which is crucial in real-world applications. Previous research has proposed a Mix-Training (MT) approach to solve the problem, however, it has never been tested in the few-shot scenario. In this paper, we investigate the possibility of using MT and other relevant methods to solve the two practical challenges together: few-shot and mixed speech. Experiments conducted on the LibriSpeech and Google Speech Command corpora demonstrate that MT is highly effective on this task when employed in either the pre-training phase or the fine-tuning phase. Moreover, combining SSL-based large-scale pre-training (HuBert) and MT fine-tuning yields very strong results in all the test conditions. |
| title | Few-Shot Keyword Spotting from Mixed Speech |
| topic | Sound |
| url | https://arxiv.org/abs/2407.06078 |