Nettet14. mai 2024 · In this work, we propose an improved consistency training paradigm of semi-supervised S2S ASR. We utilize speech chain reconstruction as the weak augmentation to generate high-quality pseudo labels. NettetTopics: multilingual ASR, low-resource NLP/ASR, privacy federated learning in ASR, semi-supervised learning in Vision / ASR, domain transfer and generalization. ... Joint masked cpc and ctc training for asr. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 3045-3049).
[2011.00093] Joint Masked CPC and CTC Training for ASR - arXiv.org
NettetJoint Masked CPC and CTC Training for ASR. Abstract. Self-supervised learning (SSL) has shown promise in learning representations of audio that are useful for automatic speech recognition (ASR). But, training SSL models like wav2vec 2.0 … Nettetend ASR model for real-world scenarios. In this work, Mask CTC model is trained using a Transformer encoder-decoder with joint training of mask prediction and CTC. During infer-ence, the target sequence is initialized with the greedy CTC out-puts and low-confidence tokens are masked based on the CTC probabilities. hager breaker price in pakistan
Joint Masked CPC And CTC Training For ASR - IEEE Xplore
Nettet18. mai 2024 · We present Mask CTC, a novel non-autoregressive end-to-end automatic speech recognition (ASR) framework, which generates a sequence by refining outputs of the connectionist temporal classification (CTC). Neural sequence-to-sequence models are usually \\textit{autoregressive}: each output token is generated by conditioning on … Nettet“Improved noisy student training for automatic speech recognition, ”Proc. Interspeech 2024, pp. 2817–2821, 2024. Joint Masked CPC and CTC Training for ASR Facebook AI Research Facebook AI Research Overview Self-supervised training for ASR requires two stages: • pre-training on unlabeled data; • fine-tuning on labeled data. Nettet30. okt. 2024 · In this paper we demonstrate a single-stage training of ASR models that can utilize both unlabeled and labeled data. During training, we alternately minimize two losses: an unsupervised masked Contrastive Predictive Coding (CPC) loss and the supervised audio-to-text alignment loss Connectionist Temporal Classification (CTC). bramford road old boys fc