Voice Conversion by Cascading Automatic Speech Recognition and Text-to-Speech Synthesis with Prosody Transfer
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Abstract (arXiv)
With the development of automatic speech recognition (ASR) and text-to-speech synthesis (TTS) technique, it's intuitive to construct a voice conversion system by cascading an ASR and TTS system. In this paper, we present a ASR-TTS method for voice conversion, which used iFLYTEK ASR engine to transcribe the source speech into text and a Transformer TTS model with WaveNet vocoder to synthesize the converted speech from the decoded text. For the TTS model, we proposed to use a prosody code to describe the prosody information other than text and speaker information contained in speech. A prosody encoder is used to extract the prosody code. During conversion, the source prosody is transferred to converted speech by conditioning the Transformer TTS model with its code. Experiments were conducted to demonstrate the effectiveness of our proposed method. Our system also obtained the best naturalness and similarity in the mono-lingual task of Voice Conversion Challenge 2020.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Sep 2020.
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Citing papers per year: 2021: 6; 2022: 2; 2023: 0; 2024: 2.
10 papers.
- 2024A Large-Scale Evaluation of Speech Foundation Modelscited by 38
- 2021S3PRL-VC: Open-source Voice Conversion Framework with Self-supervised Speech Representationscited by 22
- 2021Assem-VC: Realistic Voice Conversion by Assembling Modern Speech Synthesis Techniquescited by 11
- 2021NVC-Net: End-to-End Adversarial Voice Conversioncited by 9
- 2022A Comparative Study of Self-supervised Speech Representation Based Voice Conversioncited by 9
- 2021On Prosody Modeling for ASR+TTS based Voice Conversioncited by 3
- 2021Improved Prosodic Clustering for Multispeaker and Speaker-independent Phoneme-level Prosody Controlcited by 3
- 2022GlowVC: Mel-spectrogram space disentangling model for language-independent text-free voice conversioncited by 2
- 2024Multilingual Prosody Transfer: Comparing Supervised & Transfer Learningcited by 2
- 2021Low-Latency Real-Time Non-Parallel Voice Conversion based on Cyclic Variational Autoencoder and Multiband WaveRNN with Data-Driven Linear Predictioncited by 1
- 2024Multilingual Prosody Transfer: Comparing Supervised & Transfer Learning23 May 2024
- 2024A Large-Scale Evaluation of Speech Foundation Models15 Apr 2024
- 2022A Comparative Study of Self-supervised Speech Representation Based Voice Conversion10 Jul 2022
- 2022GlowVC: Mel-spectrogram space disentangling model for language-independent text-free voice conversion4 Jul 2022
- 2021Improved Prosodic Clustering for Multispeaker and Speaker-independent Phoneme-level Prosody Control19 Nov 2021
- 2021S3PRL-VC: Open-source Voice Conversion Framework with Self-supervised Speech Representations12 Oct 2021
- 2021On Prosody Modeling for ASR+TTS based Voice Conversion20 Jul 2021
- 2021NVC-Net: End-to-End Adversarial Voice Conversion2 Jun 2021
- 2021Low-Latency Real-Time Non-Parallel Voice Conversion based on Cyclic Variational Autoencoder and Multiband WaveRNN with Data-Driven Linear Prediction20 May 2021
- 2021Assem-VC: Realistic Voice Conversion by Assembling Modern Speech Synthesis Techniques2 Apr 2021