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- W4330338096 abstract "As human-machine voice interfaces provide easy access to increasingly intelligent machines, many state-of-the-art automatic speech recognition (ASR) systems are proposed. However, commercial ASR systems usually have poor performance on domain-specific speech especially under low-resource settings. The author works with pre-trained DeepSpeech2 and Wav2Vec2 acoustic models to develop benefit-specific ASR systems. The domain-specific data are collected using proposed semi-supervised learning annotation with little human intervention. The best performance comes from a fine-tuned Wav2Vec2-Large-LV60 acoustic model with an external KenLM, which surpasses the Google and AWS ASR systems on benefit-specific speech. The viability of using error prone ASR transcriptions as part of spoken language understanding (SLU) is also investigated. Results of a benefit-specific natural language understanding (NLU) task show that the domain-specific fine-tuned ASR system can outperform the commercial ASR systems even when its transcriptions have higher word error rate (WER), and the results between fine-tuned ASR and human transcriptions are similar." @default.
- W4330338096 created "2023-03-22" @default.
- W4330338096 creator A5004739135 @default.
- W4330338096 date "2023-03-18" @default.
- W4330338096 modified "2023-10-14" @default.
- W4330338096 title "A Deep Learning System for Domain-specific Speech Recognition" @default.
- W4330338096 doi "https://doi.org/10.48550/arxiv.2303.10510" @default.
- W4330338096 hasPublicationYear "2023" @default.
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