Evaluating Speech Intelligibility Enhancement for HMM-Based Synthetic Speech in Noise
It is possible to increase the intelligibility of speech in noise by enhancing the clean speech signal. In this paper, the authors demonstrate the effects of modifying the spectral envelope of synthetic speech according to the environmental noise. To achieve this, they modify Mel cepstral coefficients according to an intelligibility measure that accounts for glimpses of speech in noise: the Glimpse Proportion measure. They evaluate this method against a baseline synthetic voice trained only with normal speech and a topline voice trained with Lombard speech, as well as natural speech. The intelligibility of these voices was measured when mixed with speech-shaped noise and with a competing speaker at three different levels.