2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDHLT-11.1
Paper Title ASR n-best Fusion Nets
Authors Xinyue Liu, Mingda Li, Luoxin Chen, Prashan Wanigasekara, Weitong Ruan, Haidar Khan, Wael Hamza, Chengwei Su, Amazon, United States
SessionHLT-11: Language Understanding 3: Speech Understanding - General Topics
LocationGather.Town
Session Time:Thursday, 10 June, 13:00 - 13:45
Presentation Time:Thursday, 10 June, 13:00 - 13:45
Presentation Poster
Topic Human Language Technology: [HLT-UNDE] Spoken Language Understanding and Computational Semantics
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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.