Computer Science > Sound
[Submitted on 4 Sep 2023 (v1), last revised 7 Oct 2023 (this version, v2)]
Title:SememeASR: Boosting Performance of End-to-End Speech Recognition against Domain and Long-Tailed Data Shift with Sememe Semantic Knowledge
View PDFAbstract:Recently, excellent progress has been made in speech recognition. However, pure data-driven approaches have struggled to solve the problem in domain-mismatch and long-tailed data. Considering that knowledge-driven approaches can help data-driven approaches alleviate their flaws, we introduce sememe-based semantic knowledge information to speech recognition (SememeASR). Sememe, according to the linguistic definition, is the minimum semantic unit in a language and is able to represent the implicit semantic information behind each word very well. Our experiments show that the introduction of sememe information can improve the effectiveness of speech recognition. In addition, our further experiments show that sememe knowledge can improve the model's recognition of long-tailed data and enhance the model's domain generalization ability.
Submission history
From: Jiaxu Zhu [view email][v1] Mon, 4 Sep 2023 08:35:05 UTC (1,336 KB)
[v2] Sat, 7 Oct 2023 04:30:26 UTC (1,336 KB)
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