Ensemble Chinese End-to-End Spoken Language Understanding for Abnormal Event Detection from audio stream
This work addresses abnormal event detection for Chinese live audio streams, representing an incremental improvement in domain-specific SLU.
The paper tackled the problem of detecting abnormal events from Chinese audio streams by proposing an ensemble end-to-end spoken language understanding model, achieving a 9.7% increase in accuracy compared to previous models.
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 Chines, 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 proposed approached achieve 9.7% increase of accuracy compared to previous end-to-end SLU model.