Acoustic Model Fusion for End-to-end Speech Recognition

• URL: http://arxiv.org/abs/2310.07062v1
• Date: Tue, 10 Oct 2023 23:00:17 GMT
• Title: Acoustic Model Fusion for End-to-end Speech Recognition
• Authors: Zhihong Lei, Mingbin Xu, Shiyi Han, Leo Liu, Zhen Huang, Tim Ng, Yuanyuan Zhang, Ernest Pusateri, Mirko Hannemann, Yaqiao Deng, Man-Hung Siu
• Abstract summary: Speech recognition systems implicitly model all conventional ASR components, such as the acoustic model (AM) and the language model (LM) We propose the integration of an external AM into the E2E system to better address the domain mismatch. We have achieved a significant reduction in the word error rate, with an impressive drop of up to 14.3% across varied test sets.
• Score: 7.431401982826315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in deep learning and automatic speech recognition (ASR) have enabled the end-to-end (E2E) ASR system and boosted the accuracy to a new level. The E2E systems implicitly model all conventional ASR components, such as the acoustic model (AM) and the language model (LM), in a single network trained on audio-text pairs. Despite this simpler system architecture, fusing a separate LM, trained exclusively on text corpora, into the E2E system has proven to be beneficial. However, the application of LM fusion presents certain drawbacks, such as its inability to address the domain mismatch issue inherent to the internal AM. Drawing inspiration from the concept of LM fusion, we propose the integration of an external AM into the E2E system to better address the domain mismatch. By implementing this novel approach, we have achieved a significant reduction in the word error rate, with an impressive drop of up to 14.3% across varied test sets. We also discovered that this AM fusion approach is particularly beneficial in enhancing named entity recognition.

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