Deep neural network based wake-up-word speech recognition with two-stage detection

This paper presents a novel far-field voice trigger algorithm utilizing DNN with the objective function of state-level minimum Bayes risk for training, customizing the decoding network to absorb the ambient noise and background speech. We adopt a two-stage classification strategy to integrate the phonetic knowledge and model-based classification into detecting wake-up words. Experimental results of the online test show that it can provide a higher than 90% accuracy and meanwhile false alarms are less than once per nine hours in the noisy home environments where the sound pressure level is about 80dB.