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- W3136178271 abstract "Current spoken language understanding systems heavily rely on the best hypothesis (ASR 1-best) generated by automatic speech recognition, which is used as the input for downstream models such as natural language understanding (NLU) modules. However, the potential errors and misrecognition in ASR 1-best raise challenges to NLU. It is usually difficult for NLU models to recover from ASR errors without additional signals, which leads to suboptimal SLU performance. This paper proposes a fusion network to jointly consider ASR n-best hypotheses for enhanced robustness to ASR errors. Our experiments on Alexa data show that our model achieved 21.71% error reduction compared to baseline trained on transcription for domain classification." @default.
- W3136178271 created "2021-03-29" @default.
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- W3136178271 date "2021-06-06" @default.
- W3136178271 modified "2023-09-26" @default.
- W3136178271 title "ASR N-Best Fusion Nets" @default.
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- W3136178271 doi "https://doi.org/10.1109/icassp39728.2021.9414806" @default.
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