RNN-T BASED OPEN-VOCABULARY KEYWORD SPOTTING IN MANDARIN WITH MULTI-LEVEL DETECTION

Despite the recent prevalence of keyword spotting (KWS) in smart-home, open-vocabulary KWS remains a keen but unmet need among the users. In this paper, we propose an RNN Transducer (RNN-T) based keyword spotting system with a constrained attention mechanism biasing module that biases the RNN-T model towards a specific keyword of interest. The atonal syllables are adopted as the modeling units, which addresses the out-of-vocabulary (OOV) problem. A multi-level detection is applied to the posterior probabilities for the judgement. Evaluating on the AISHELL-2 dataset shows our proposed method outperforms the RNN-T-based approach by 2.70% in false reject rate (FRR) at 1 false alarm (FA) per hour. We further provide insights into the role of each stage of the detection cascade, where most negative samples are filtered out by the first stage with high computational efficiency.