Maximum intelligibility-based close-loop speech synthesis framework for noisy environments

This paper proposes a maximum intelligibility (MI)-based close-loop speech synthesis framework to actively compensate for the distortion of background noises. In this framework, an extra environmental noise-sensing microphone and an automatic speech recognition (ASR) module are utilized to approximate a subjective intelligibility measure. The hidden Markov model-based speech synthesis system (HTS) is then online adjusted by using the MI-based model adaptation algorithm. Experimental results of two subjective listening tests in noisy environments show that the proposed approach obtains 64% of the votes in an A/B preference test and helps the participants reduce word dictation errors by relative 26% when compared to an HTS baseline.