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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.03538 |
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| _version_ | 1866911750255804416 |
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| author | Chen, Xi Pei, Jiakun Xue, Liumeng Zhang, Mingyang |
| author_facet | Chen, Xi Pei, Jiakun Xue, Liumeng Zhang, Mingyang |
| contents | Accent conversion aims to convert the accent of a source speech to a target accent, meanwhile preserving the speaker's identity. This paper introduces a novel non-autoregressive framework for accent conversion that learns accent-agnostic linguistic representations and employs them to convert the accent in the source speech. Specifically, the proposed system aligns speech representations with linguistic representations obtained from Text-to-Speech (TTS) systems, enabling training of the accent voice conversion model on non-parallel data. Furthermore, we investigate the effectiveness of a pretraining strategy on native data and different acoustic features within our proposed framework. We conduct a comprehensive evaluation using both subjective and objective metrics to assess the performance of our approach. The evaluation results highlight the benefits of the pretraining strategy and the incorporation of richer semantic features, resulting in significantly enhanced audio quality and intelligibility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03538 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transfer the linguistic representations from TTS to accent conversion with non-parallel data Chen, Xi Pei, Jiakun Xue, Liumeng Zhang, Mingyang Computation and Language Sound Audio and Speech Processing Accent conversion aims to convert the accent of a source speech to a target accent, meanwhile preserving the speaker's identity. This paper introduces a novel non-autoregressive framework for accent conversion that learns accent-agnostic linguistic representations and employs them to convert the accent in the source speech. Specifically, the proposed system aligns speech representations with linguistic representations obtained from Text-to-Speech (TTS) systems, enabling training of the accent voice conversion model on non-parallel data. Furthermore, we investigate the effectiveness of a pretraining strategy on native data and different acoustic features within our proposed framework. We conduct a comprehensive evaluation using both subjective and objective metrics to assess the performance of our approach. The evaluation results highlight the benefits of the pretraining strategy and the incorporation of richer semantic features, resulting in significantly enhanced audio quality and intelligibility. |
| title | Transfer the linguistic representations from TTS to accent conversion with non-parallel data |
| topic | Computation and Language Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2401.03538 |