Joint Automatic Speech Recognition And Structure Learning For Better Speech Understanding

• URL: http://arxiv.org/abs/2501.07329v2
• Date: Fri, 17 Jan 2025 17:53:27 GMT
• Title: Joint Automatic Speech Recognition And Structure Learning For Better Speech Understanding
• Authors: Jiliang Hu, Zuchao Li, Mengjia Shen, Haojun Ai, Sheng Li, Jun Zhang,
• Abstract summary: We propose a joint speech recognition and structure learning framework (JSRSL), which can accurately transcribe speech and extract structured content simultaneously. The results show that our proposed method outperforms the traditional sequence-to-sequence method in both transcription and extraction capabilities.
• Score: 25.986288893402225
• License:
• Abstract: Spoken language understanding (SLU) is a structure prediction task in the field of speech. Recently, many works on SLU that treat it as a sequence-to-sequence task have achieved great success. However, This method is not suitable for simultaneous speech recognition and understanding. In this paper, we propose a joint speech recognition and structure learning framework (JSRSL), an end-to-end SLU model based on span, which can accurately transcribe speech and extract structured content simultaneously. We conduct experiments on name entity recognition and intent classification using the Chinese dataset AISHELL-NER and the English dataset SLURP. The results show that our proposed method not only outperforms the traditional sequence-to-sequence method in both transcription and extraction capabilities but also achieves state-of-the-art performance on the two datasets.

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