XLSR-Transducer: Streaming ASR for Self-Supervised Pretrained Models

Overview of "XLSR-Transducer: Streaming ASR for Self-Supervised Pretrained Models"

The paper "XLSR-Transducer: Streaming ASR for Self-Supervised Pretrained Models" presents a noteworthy exploration into the adaptation of self-supervised pretrained models for streaming automatic speech recognition (ASR) using the transducer architecture. The research addresses the inherent limitation of popular pretrained models, such as XLSR-53, which are traditionally trained with full attention context and thus, are unsuitable for real-time ASR applications.

Key Contributions

1. Introduction of XLSR-Transducer: The paper introduces the XLSR-Transducer, a streaming ASR model that utilizes the XLSR-53 model as an encoder within the transducer architecture. The proposed model achieves a significant 4% absolute improvement in word error rate (WER) over the Whisper large-v2 model and an 8% improvement over a Zipformer transducer model trained from scratch on the AMI dataset.
2. Attention Masking for Streaming Capability: To enable streaming capabilities, the authors explore various attention masking patterns in the self-attention calculation of transformer layers within the XLSR-53 model. These strategies include chunked masking and chunked masking with variable left context chunks, which allow for efficient streaming decoding by limiting the context frames considered during self-attention computation.
3. Evaluation Framework: The performance of the XLSR-Transducer is validated on the AMI dataset and five languages from the CommonVoice dataset under low-resource scenarios. This broad evaluation establishes the model's effectiveness across different languages and data conditions.
4. Introduction of Attention Sinks in ASR: The research explores the utilization of attention sinks, a phenomenon where transformer layers learn to disproportionately attend to initial inputs during streaming inference. This novel application in ASR leads to a relative 12% improvement in WER by reducing the left context needed during inference by half.

Experimental Results

The results on the AMI dataset reveal that the XLSR-Transducer significantly outperforms competitive models in both streaming and non-streaming ASR settings. For non-streaming ASR, the XLSR-Transducer achieves a WER of 12.7%, relatively outperforming the Whisper large-v2 model by 25% and the Zipformer model by 39%. For streaming ASR, the model trained with multi-chunk masking achieves a WER of 17.7% with a 320 ms chunk size and 14.2% with a 1280 ms chunk size.

Similarly, the evaluation on the CommonVoice dataset, which includes five non-English languages, demonstrates the model's robustness. The XLSR-Transducer consistently achieves competitive WERs across different chunk sizes. Importantly, the non-streaming decoding of the streaming-trained models shows negligible degradation in performance, highlighting the model's versatility.

Implications and Future Work

The introduction of the XLSR-Transducer model signifies a substantial improvement in leveraging self-supervised pretrained models for streaming ASR. This approach mitigates the dependency on large-scale in-domain supervised data, making it especially advantageous for low-resource languages and applications.

The implementation of attention sinks to streamline the self-attention mechanism provides an innovative means to balance computational efficiency and performance. This technique can be further explored to enhance real-time ASR systems, potentially extending to other domains where transformer-based models are employed.

Future developments could involve expanding this methodology to broader datasets and more diverse linguistic contexts, improving generalizability. Additionally, optimizing the computational aspects of streaming ASR through hardware-integration techniques or model distillation could further enhance the real-time applicability of XLSR-Transducer.

Conclusion

This paper makes a significant contribution to the field of ASR by adapting self-supervised learning models for effective streaming. The combination of XLSR-53 with transducer architecture and the introduction of innovative attention mechanisms results in a robust ASR system that shows significant improvements in WER while maintaining computational efficiency. The findings set a precedent for future research in streaming ASR, particularly in integrating advanced pretrained models with practical, real-time applications.