Silence is golden: Modeling non-speech events in WFST-based dynamic network decoders

Models for silence are a fundamental part of continuous speech recognition systems. Depending on application requirements, audio data segmentation, and availability of detailed training data annotations, it may be necessary or beneficial to differentiate between other non-speech events, for example breath and background noise. The integration of multiple non-speech models in a WFST-based dynamic network decoder is not straightforward, because these models do not perfectly fit in the transducer framework. This paper describes several options for the transducer construction with multiple non-speech models, shows their considerable different characteristics in memory and runtime efficiency, and analyzes the impact on the recognition performance.