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- W3093452977 abstract "Conventional spoken language understanding (SLU) consist of two stages, the first stage maps speech to text by automatic speech recognition (ASR), and the second stage maps text to intent by natural language understanding (NLU). End-to-end SLU maps speech directly to intent through a single deep learning model. Previous end-to-end SLU models are primarily used for English environment due to lacking large scale SLU dataset in Chinese, and use only one ASR model to extract features from speech. With the help of Kuaishou technology, a large scale SLU dataset in Chinese is collected to detect abnormal event in their live audio stream. Based on this dataset, this paper proposed a ensemble end-to-end SLU model used for Chinese environment. This ensemble SLU models extracted hierarchies features using multiple pre-trained ASR models, leading to better representation of phoneme level and word level information. This work also showed a feasible way to build a end-to-end SLU system without transcribing audios. The proposed approached achieve 9.7% increase of accuracy compared to previous end-to-end SLU model." @default.
- W3093452977 created "2020-10-22" @default.
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- W3093452977 date "2021-09-24" @default.
- W3093452977 modified "2023-10-12" @default.
- W3093452977 title "Ensemble Chinese End-to-End Spoken Language Understanding for Abnormal Event Detection from Audio Stream" @default.
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- W3093452977 doi "https://doi.org/10.1145/3490322.3490323" @default.
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