Learning Speech Representation From Contrastive Token-Acoustic Pretraining (2309.00424v5)
Abstract: For fine-grained generation and recognition tasks such as minimally-supervised text-to-speech (TTS), voice conversion (VC), and automatic speech recognition (ASR), the intermediate representations extracted from speech should serve as a "bridge" between text and acoustic information, containing information from both modalities. The semantic content is emphasized, while the paralinguistic information such as speaker identity and acoustic details should be de-emphasized. However, existing methods for extracting fine-grained intermediate representations from speech suffer from issues of excessive redundancy and dimension explosion. Contrastive learning is a good method for modeling intermediate representations from two modalities. However, existing contrastive learning methods in the audio field focus on extracting global descriptive information for downstream audio classification tasks, making them unsuitable for TTS, VC, and ASR tasks. To address these issues, we propose a method named "Contrastive Token-Acoustic Pretraining (CTAP)", which uses two encoders to bring phoneme and speech into a joint multimodal space, learning how to connect phoneme and speech at the frame level. The CTAP model is trained on 210k speech and phoneme pairs, achieving minimally-supervised TTS, VC, and ASR. The proposed CTAP method offers a promising solution for fine-grained generation and recognition downstream tasks in speech processing. We provide a website with audio samples.
- “Self-supervised speech representation learning: A review,” IEEE Journal of Selected Topics in Signal Processing, 2022.
- “wav2vec 2.0: A framework for self-supervised learning of speech representations,” Advances in neural information processing systems, 2020.
- “Wav2vec-c: A self-supervised model for speech representation learning,” arXiv preprint arXiv:2103.08393, 2021.
- “vq-wav2vec: Self-supervised learning of discrete speech representations,” arXiv preprint arXiv:1910.05453, 2019.
- “Hubert: Self-supervised speech representation learning by masked prediction of hidden units,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 3451–3460, 2021.
- “W2v-bert: Combining contrastive learning and masked language modeling for self-supervised speech pre-training,” in 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2021, pp. 244–250.
- “Speak, read and prompt: High-fidelity text-to-speech with minimal supervision,” arXiv preprint arXiv:2302.03540, 2023.
- “Minimally-supervised speech synthesis with conditional diffusion model and language model: A comparative study of semantic coding,” arXiv preprint arXiv:2307.15484, 2023.
- “Phonetic posteriorgrams for many-to-one voice conversion without parallel data training,” in 2016 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2016, pp. 1–6.
- “Learning transferable visual models from natural language supervision,” in International conference on machine learning. PMLR, 2021, pp. 8748–8763.
- “Florence: A new foundation model for computer vision,” arXiv preprint arXiv:2111.11432, 2021.
- “Scaling up visual and vision-language representation learning with noisy text supervision,” in International conference on machine learning. PMLR, 2021, pp. 4904–4916.
- “Wav2clip: Learning robust audio representations from clip,” in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022, pp. 4563–4567.
- “Audioclip: Extending clip to image, text and audio,” in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022, pp. 976–980.
- “Clap learning audio concepts from natural language supervision,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023, pp. 1–5.
- “Improving prosody for cross-speaker style transfer by semi-supervised style extractor and hierarchical modeling in speech synthesis,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023, pp. 1–5.
- “High fidelity neural audio compression,” ArXiv, vol. abs/2210.13438, 2022.
- “Robust speech recognition via large-scale weak supervision,” in International Conference on Machine Learning. PMLR, 2023, pp. 28492–28518.
- “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 7132–7141.
- “Adam: A method for stochastic optimization,” CoRR, vol. abs/1412.6980, 2014.
- “Aishell-3: A multi-speaker mandarin tts corpus and the baselines,” arXiv preprint arXiv:2010.11567, 2020.
- Chunyu Qiang (21 papers)
- Hao Li (803 papers)
- Yixin Tian (2 papers)
- Ruibo Fu (54 papers)
- Tao Wang (700 papers)
- Longbiao Wang (46 papers)
- Jianwu Dang (41 papers)