Twin-HMM-based non-intrusive speech intelligibility prediction

Abstract

Most of the objective measures employed for speech intelligibility prediction require a clean reference signal, which is not accessible in all realistic scenarios. In this paper, we propose to re-synthesize the relevant features of the clean signal using only the noisy speech signal and utilize them inside an intelligibility prediction framework which requires a reference. A statistical model called twin hidden Markov model (THMM) is used to synthesize the clean speech features. For the intelligibility prediction framework, the short-time objective intelligibility (STOI) measure is used as an accurate and well-known method. The experimental results show a high correlation between the twin-HMM-based STOI (THMMB-STOI) and the human speech recognition results, even slightly outperforming the conventional STOI predictions computed using the actual clean reference signals.