Exploring Sequence-to-Sequence Transformer-Transducer Models for Keyword Spotting
This provides an incremental improvement for keyword spotting tasks in speech recognition, enhancing flexibility in training.
The paper tackles keyword spotting by adapting a sequence-to-sequence Transformer-Transducer ASR system to detect keywords using a special token and a decision function, achieving similar performance to conventional systems while offering more flexibility.
In this paper, we present a novel approach to adapt a sequence-to-sequence Transformer-Transducer ASR system to the keyword spotting (KWS) task. We achieve this by replacing the keyword in the text transcription with a special token <kw> and training the system to detect the <kw> token in an audio stream. At inference time, we create a decision function inspired by conventional KWS approaches, to make our approach more suitable for the KWS task. Furthermore, we introduce a specific keyword spotting loss by adapting the sequence-discriminative Minimum Bayes-Risk training technique. We find that our approach significantly outperforms ASR based KWS systems. When compared with a conventional keyword spotting system, our proposal has similar performance while bringing the advantages and flexibility of sequence-to-sequence training. Additionally, when combined with the conventional KWS system, our approach can improve the performance at any operation point.