# Improving Streaming Transformer Based ASR Under a Framework of Self-supervised Learning

arXiv: https://arxiv.org/abs/2109.07327 · HTML: https://mlsanity.com/paper/2109.07327/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2021.

## Built on by (most cited first, 12 of 12)

- [Self-Supervised Speech Representation Learning: A Review](https://mlsanity.com/paper/2205.10643/) (2022)
- [Transformers in Speech Processing: A Survey](https://mlsanity.com/paper/2303.11607/) (2023)
- [MelHuBERT: A simplified HuBERT on Mel spectrograms](https://mlsanity.com/paper/2211.09944/) (2022)
- [Knowledge Distillation for Neural Transducers from Large Self-Supervised Pre-trained Models](https://mlsanity.com/paper/2110.03334/) (2021)
- [Improving Hybrid CTC/Attention End-to-end Speech Recognition with Pretrained Acoustic and Language Model](https://mlsanity.com/paper/2112.07254/) (2021)
- [DistillW2V2: A Small and Streaming Wav2vec 2.0 Based ASR Model](https://mlsanity.com/paper/2303.09278/) (2023)
- [Streaming on-device detection of device directed speech from voice and touch-based invocation](https://mlsanity.com/paper/2110.04656/) (2021)
- [Decoupled Structure for Improved Adaptability of End-to-End Models](https://mlsanity.com/paper/2308.13345/) (2023)
- [NEST-RQ: Next Token Prediction for Speech Self-Supervised Pre-Training](https://mlsanity.com/paper/2409.08680/) (2024)
- [Revisiting the Entropy Semiring for Neural Speech Recognition](https://mlsanity.com/paper/2312.10087/) (2023)
- [QS-TTS: Towards Semi-Supervised Text-to-Speech Synthesis via Vector-Quantized Self-Supervised Speech Representation Learning](https://mlsanity.com/paper/2309.00126/) (2023)
- [Dynamic Latency for CTC-Based Streaming Automatic Speech Recognition With Emformer](https://mlsanity.com/paper/2203.15613/) (2022)

## Abstract (arXiv)

Recently self-supervised learning has emerged as an effective approach to improve the performance of automatic speech recognition (ASR). Under such a framework, the neural network is usually pre-trained with massive unlabeled data and then fine-tuned with limited labeled data. However, the non-streaming architecture like bidirectional transformer is usually adopted by the neural network to achieve competitive results, which can not be used in streaming scenarios. In this paper, we mainly focus on improving the performance of streaming transformer under the self-supervised learning framework. Specifically, we propose a novel two-stage training method during fine-tuning, which combines knowledge distilling and self-training. The proposed training method achieves 16.3% relative word error rate (WER) reduction on Librispeech noisy test set. Finally, by only using the 100h clean subset of Librispeech as the labeled data and the rest (860h) as the unlabeled data, our streaming transformer based model obtains competitive WERs 3.5/8.7 on Librispeech clean/noisy test sets.