Simultaneous Flexible Keyword Detection and Text-dependent Speaker Recognition for Low-resource Devices

This paper proposes a new method for simultaneous flexible keyword detection and text-dependent speaker identification using a recognized keyword. The purpose is to identify a speaker from among a set of preregistered speakers on the basis of a short-command utterance in an office or home on low-resource chip devices. The first contribution is to construct the process that includes a neural network (NN) and a customized Viterbi-based algorithm for flexible keyword detection, and Gaussian mixture models (GMMs) for speaker identification. Outputs of a middle layer in the NN and alignment information for keyword detection are also used for creating feature vectors for speaker GMMs. The second contribution is to apply DropConnect in speaker-modeling uncertainties of the Bayesian NN that is used for speaker reacognition. It results in robust speaker models when enrollment utterances are few. Evaluation was conducted using 39 Japanese keywords by 100 speakers. Recognition performance was measured on the basis of false acceptances and false rejects using keyword utterances. Speaker identification for 100 pre-registered speakers for recognized keywords was simultaneously evaluated. The identification rate when using a conventional i-vector method was 71.22%. By contrast, the identification rate of the proposed method was 89.29% while using low-cost resources.