XLSR-Transducer: Streaming ASR for Self-Supervised Pretrained Models
| Type of publication: | Idiap-RR |
| Citation: | Kumar_Idiap-RR-08-2024 |
| Number: | Idiap-RR-08-2024 |
| Year: | 2024 |
| Month: | 8 |
| Institution: | Idiap |
| Abstract: | Self-supervised pretrained models exhibit competitive performance in automatic speech recognition on finetuning, even with limited in-domain supervised data for training. However, popular pretrained models are not suitable for streaming ASR because they are trained with full attention context. In this paper, we introduce XLSR-Transducer, where the XLSR-53 model is used as encoder in transducer setup. Our experiments on the AMI dataset reveal that the XLSR-Transducer achieves 4% absolute WER improvement over Whisper large-v2 and 8% over a Zipformer transducer model trained from scratch. To enable streaming capabilities, we investigate different attention masking patterns in the self-attention computation of transformer layers within the XLSR-53 model. We validate XLSR-Transducer on AMI and 5 languages from CommonVoice under low-resource scenarios. Finally, with the introduction of attention sinks, we reduce the left context by half while achieving a relative 12% improvement in WER. |
| URL: | https://arxiv.org/abs/2407.044... |
| Keywords: | |
| Authors | |
| Added by: | [ADM] |
| Total mark: | 0 |
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