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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | https://arxiv.org/abs/2401.09315 |
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| _version_ | 1866911759715008512 |
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| author | López-Espejo, Iván Joglekar, Aditya Peinado, Antonio M. Jensen, Jesper |
| author_facet | López-Espejo, Iván Joglekar, Aditya Peinado, Antonio M. Jensen, Jesper |
| contents | Pre-emphasis filtering, compensating for the natural energy decay of speech at higher frequencies, has been considered as a common pre-processing step in a number of speech processing tasks over the years. In this work, we demonstrate, for the first time, that pre-emphasis filtering may also be used as a simple and computationally-inexpensive way to leverage deep neural network-based speech enhancement performance. Particularly, we look into pre-emphasizing the estimated and actual clean speech prior to loss calculation so that different speech frequency components better mirror their perceptual importance during the training phase. Experimental results on a noisy version of the TIMIT dataset show that integrating the pre-emphasis-based methodology at hand yields relative estimated speech quality improvements of up to 4.6% and 3.4% for noise types seen and unseen, respectively, during the training phase. Similar to the case of pre-emphasis being considered as a default pre-processing step in classical automatic speech recognition and speech coding systems, the pre-emphasis-based methodology analyzed in this article may potentially become a default add-on for modern speech enhancement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_09315 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On Speech Pre-emphasis as a Simple and Inexpensive Method to Boost Speech Enhancement López-Espejo, Iván Joglekar, Aditya Peinado, Antonio M. Jensen, Jesper Audio and Speech Processing Pre-emphasis filtering, compensating for the natural energy decay of speech at higher frequencies, has been considered as a common pre-processing step in a number of speech processing tasks over the years. In this work, we demonstrate, for the first time, that pre-emphasis filtering may also be used as a simple and computationally-inexpensive way to leverage deep neural network-based speech enhancement performance. Particularly, we look into pre-emphasizing the estimated and actual clean speech prior to loss calculation so that different speech frequency components better mirror their perceptual importance during the training phase. Experimental results on a noisy version of the TIMIT dataset show that integrating the pre-emphasis-based methodology at hand yields relative estimated speech quality improvements of up to 4.6% and 3.4% for noise types seen and unseen, respectively, during the training phase. Similar to the case of pre-emphasis being considered as a default pre-processing step in classical automatic speech recognition and speech coding systems, the pre-emphasis-based methodology analyzed in this article may potentially become a default add-on for modern speech enhancement. |
| title | On Speech Pre-emphasis as a Simple and Inexpensive Method to Boost Speech Enhancement |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2401.09315 |