Accented Text-to-Speech Synthesis with a Conditional Variational Autoencoder
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866910623791579136 |
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| author | Melechovsky, Jan Mehrish, Ambuj Sisman, Berrak Herremans, Dorien |
| author_facet | Melechovsky, Jan Mehrish, Ambuj Sisman, Berrak Herremans, Dorien |
| contents | Accent plays a significant role in speech communication, influencing one's capability to understand as well as conveying a person's identity. This paper introduces a novel and efficient framework for accented Text-to-Speech (TTS) synthesis based on a Conditional Variational Autoencoder. It has the ability to synthesize a selected speaker's voice, and convert this to any desired target accent. Our thorough experiments validate the effectiveness of the proposed framework using both objective and subjective evaluations. The results also show remarkable performance in terms of the model's ability to manipulate accents in the synthesized speech. Overall, our proposed framework presents a promising avenue for future accented TTS research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_03316 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Accented Text-to-Speech Synthesis with a Conditional Variational Autoencoder Melechovsky, Jan Mehrish, Ambuj Sisman, Berrak Herremans, Dorien Audio and Speech Processing Machine Learning Sound Accent plays a significant role in speech communication, influencing one's capability to understand as well as conveying a person's identity. This paper introduces a novel and efficient framework for accented Text-to-Speech (TTS) synthesis based on a Conditional Variational Autoencoder. It has the ability to synthesize a selected speaker's voice, and convert this to any desired target accent. Our thorough experiments validate the effectiveness of the proposed framework using both objective and subjective evaluations. The results also show remarkable performance in terms of the model's ability to manipulate accents in the synthesized speech. Overall, our proposed framework presents a promising avenue for future accented TTS research. |
| title | Accented Text-to-Speech Synthesis with a Conditional Variational Autoencoder |
| topic | Audio and Speech Processing Machine Learning Sound |
| url | https://arxiv.org/abs/2211.03316 |