DNN-based Feature Transformation for Speech Recognition Using Throat Microphone

In this paper, we focus on utilizing a throat microphone as noise robust device because its signal is much less affected by surrounding noise than a conventional acoustic microphone signal. However, it can only record narrow frequency bands, and the microphone characteristics are also different from characteristics of acoustic microphone. Therefore, speech recognition performance is greatly degraded when a throat microphone is used as it is instead of a conventional acoustic microphone. To overcome this problem, we propose using a deep neural network (DNN)-based feature transformation method while also using model adaptation. We conducted a continuous digit recognition experiment. The result revealed that the proposed method improved the word error rate (WER) of using the throat microphone from 41.4% to 17.6%.