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SENS-ASR: Semantic Embedding injection in Neural-transducer for Streaming Automatic Speech Recognition

arXiv:2603.10005v1 Announce Type: cross Abstract: Many Automatic Speech Recognition (ASR) applications require streaming processing of the audio data. In streaming mode, ASR systems need to start transcribing the input stream before it is complete, i.e., the systems have to process a stream of inputs with a limited (or no) future context. Compared to offline mode, this reduction of the future context degrades the performance of Streaming-ASR systems, especially while working with low-latency constraint. In this work, we present SENS-ASR, an approach to enhance the transcription quality of Streaming-ASR by reinforcing the acoustic information with semantic information. This semantic information is extracted from the available past frame-embeddings by a context module. This module is trained using knowledge distillation from a sentence embedding Language Model fine-tuned on the training dataset transcriptions. Experiments on standard datasets show that SENS-ASR significantly improves th

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Youness Dkhissi (LIUM), Valentin Vielzeuf (LIUM), Elys Allesiardo (LIUM), Anthony Larcher (LIUM) · · 1 min read · 11 views
#cs.CL #cs.AI

arXiv:2603.10005v1 Announce Type: cross Abstract: Many Automatic Speech Recognition (ASR) applications require streaming processing of the audio data. In streaming mode, ASR systems need to start transcribing the input stream before it is complete, i.e., the systems have to process a stream of inputs with a limited (or no) future context. Compared to offline mode, this reduction of the future context degrades the performance of Streaming-ASR systems, especially while working with low-latency constraint. In this work, we present SENS-ASR, an approach to enhance the transcription quality of Streaming-ASR by reinforcing the acoustic information with semantic information. This semantic information is extracted from the available past frame-embeddings by a context module. This module is trained using knowledge distillation from a sentence embedding Language Model fine-tuned on the training dataset transcriptions. Experiments on standard datasets show that SENS-ASR significantly improves the Word Error Rate on small-chunk streaming scenarios.

Executive Summary

The article introduces SENS-ASR, a novel approach to enhance the transcription quality of Streaming Automatic Speech Recognition (ASR) systems by injecting semantic information into the neural transducer. This is achieved through a context module trained using knowledge distillation from a sentence embedding Language Model. The results show significant improvement in Word Error Rate, particularly in small-chunk streaming scenarios. This innovation addresses the challenges of streaming ASR, where limited future context degrades performance, especially under low-latency constraints.

Key Points

  • ▸ SENS-ASR integrates semantic information into the neural transducer for improved transcription quality
  • ▸ A context module is trained using knowledge distillation from a sentence embedding Language Model
  • ▸ Experiments demonstrate significant improvement in Word Error Rate for small-chunk streaming scenarios

Merits

Improved Transcription Accuracy

SENS-ASR enhances the transcription quality of Streaming-ASR systems, addressing a critical challenge in the field.

Demerits

Complexity and Computational Requirements

The integration of semantic information and the training of the context module may increase the computational complexity and requirements of the system.

Expert Commentary

The SENS-ASR approach represents a significant advancement in the field of Streaming Automatic Speech Recognition. By leveraging semantic information to enhance transcription quality, this innovation has the potential to improve the accuracy and usability of real-time speech recognition systems. However, further research is needed to fully explore the implications of this technology and to address potential challenges related to computational complexity and requirements. The use of knowledge distillation from a sentence embedding Language Model is a notable aspect of this work, highlighting the importance of interdisciplinary approaches in advancing speech recognition technology.

Recommendations

  • ✓ Further investigation into the optimization of the context module and the integration of semantic information to minimize computational complexity
  • ✓ Exploration of the application of SENS-ASR in diverse real-world scenarios to assess its robustness and generalizability

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