Noise-robust zero-shot text-to-speech synthesis conditioned on self-supervised speech-representation model with adapters Article Swipe
Ken‐ichi Fujita
,
Hiroshi Satō
,
Takanori Ashihara
,
Hiroki Kanagawa
,
Marc Delcroix
,
Takafumi Moriya
,
Yusuke Ijima
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.05111
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.05111
The zero-shot text-to-speech (TTS) method, based on speaker embeddings extracted from reference speech using self-supervised learning (SSL) speech representations, can reproduce speaker characteristics very accurately. However, this approach suffers from degradation in speech synthesis quality when the reference speech contains noise. In this paper, we propose a noise-robust zero-shot TTS method. We incorporated adapters into the SSL model, which we fine-tuned with the TTS model using noisy reference speech. In addition, to further improve performance, we adopted a speech enhancement (SE) front-end. With these improvements, our proposed SSL-based zero-shot TTS achieved high-quality speech synthesis with noisy reference speech. Through the objective and subjective evaluations, we confirmed that the proposed method is highly robust to noise in reference speech, and effectively works in combination with SE.
Related Topics
Concepts
Computer science
Speech recognition
Noise (video)
Speech synthesis
Speech enhancement
Zero (linguistics)
Representation (politics)
Voice activity detection
Quality (philosophy)
Artificial intelligence
Speech processing
Noise reduction
Image (mathematics)
Physics
Law
Linguistics
Politics
Quantum mechanics
Political science
Philosophy
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2401.05111
- https://arxiv.org/pdf/2401.05111
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390832512
All OpenAlex metadata
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https://openalex.org/W4390832512Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2401.05111Digital Object Identifier
- Title
-
Noise-robust zero-shot text-to-speech synthesis conditioned on self-supervised speech-representation model with adaptersWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-01-10Full publication date if available
- Authors
-
Ken‐ichi Fujita, Hiroshi Satō, Takanori Ashihara, Hiroki Kanagawa, Marc Delcroix, Takafumi Moriya, Yusuke IjimaList of authors in order
- Landing page
-
https://arxiv.org/abs/2401.05111Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2401.05111Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2401.05111Direct OA link when available
- Concepts
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Computer science, Speech recognition, Noise (video), Speech synthesis, Speech enhancement, Zero (linguistics), Representation (politics), Voice activity detection, Quality (philosophy), Artificial intelligence, Speech processing, Noise reduction, Image (mathematics), Physics, Law, Linguistics, Politics, Quantum mechanics, Political science, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.method, | 4 |
| abstract_inverted_index.method. | 50 |
| abstract_inverted_index.propose | 45 |
| abstract_inverted_index.quality | 34 |
| abstract_inverted_index.speaker | 7, 21 |
| abstract_inverted_index.speech, | 117 |
| abstract_inverted_index.speech. | 68, 97 |
| abstract_inverted_index.suffers | 28 |
| abstract_inverted_index.However, | 25 |
| abstract_inverted_index.achieved | 90 |
| abstract_inverted_index.adapters | 53 |
| abstract_inverted_index.approach | 27 |
| abstract_inverted_index.contains | 39 |
| abstract_inverted_index.learning | 15 |
| abstract_inverted_index.proposed | 86, 108 |
| abstract_inverted_index.SSL-based | 87 |
| abstract_inverted_index.addition, | 70 |
| abstract_inverted_index.confirmed | 105 |
| abstract_inverted_index.extracted | 9 |
| abstract_inverted_index.objective | 100 |
| abstract_inverted_index.reference | 11, 37, 67, 96, 116 |
| abstract_inverted_index.reproduce | 20 |
| abstract_inverted_index.synthesis | 33, 93 |
| abstract_inverted_index.zero-shot | 1, 48, 88 |
| abstract_inverted_index.embeddings | 8 |
| abstract_inverted_index.fine-tuned | 60 |
| abstract_inverted_index.front-end. | 81 |
| abstract_inverted_index.subjective | 102 |
| abstract_inverted_index.accurately. | 24 |
| abstract_inverted_index.combination | 122 |
| abstract_inverted_index.degradation | 30 |
| abstract_inverted_index.effectively | 119 |
| abstract_inverted_index.enhancement | 79 |
| abstract_inverted_index.evaluations, | 103 |
| abstract_inverted_index.high-quality | 91 |
| abstract_inverted_index.incorporated | 52 |
| abstract_inverted_index.noise-robust | 47 |
| abstract_inverted_index.performance, | 74 |
| abstract_inverted_index.improvements, | 84 |
| abstract_inverted_index.text-to-speech | 2 |
| abstract_inverted_index.characteristics | 22 |
| abstract_inverted_index.self-supervised | 14 |
| abstract_inverted_index.representations, | 18 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.5699999928474426 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |