Multitaper mel-spectrograms for keyword spotting
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866911945859268608 |
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| author | de Souza, Douglas Baptista Bakri, Khaled Jamal Ferreira, Fernanda Inacio, Juliana |
| author_facet | de Souza, Douglas Baptista Bakri, Khaled Jamal Ferreira, Fernanda Inacio, Juliana |
| contents | Keyword spotting (KWS) is one of the speech recognition tasks most sensitive to the quality of the feature representation. However, the research on KWS has traditionally focused on new model topologies, putting little emphasis on other aspects like feature extraction. This paper investigates the use of the multitaper technique to create improved features for KWS. The experimental study is carried out for different test scenarios, windows and parameters, datasets, and neural networks commonly used in embedded KWS applications. Experiment results confirm the advantages of using the proposed improved features. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04662 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multitaper mel-spectrograms for keyword spotting de Souza, Douglas Baptista Bakri, Khaled Jamal Ferreira, Fernanda Inacio, Juliana Audio and Speech Processing Machine Learning Keyword spotting (KWS) is one of the speech recognition tasks most sensitive to the quality of the feature representation. However, the research on KWS has traditionally focused on new model topologies, putting little emphasis on other aspects like feature extraction. This paper investigates the use of the multitaper technique to create improved features for KWS. The experimental study is carried out for different test scenarios, windows and parameters, datasets, and neural networks commonly used in embedded KWS applications. Experiment results confirm the advantages of using the proposed improved features. |
| title | Multitaper mel-spectrograms for keyword spotting |
| topic | Audio and Speech Processing Machine Learning |
| url | https://arxiv.org/abs/2407.04662 |