Being able to estimate speech intelligibility without the need for listening tests would confer great benefits for a wide range of speech processing applications. Many attempts have therefore been made to introduce an objective, and ideally referencefree measure for this purpose. Most works analyze speech intelligibility prediction (SIP) methods from a macroscopic point of view, averaging over longer time spans. This paper, in contrast, presents a theoretical framework for the microscopic evaluation of SIP methods. Within our framework, a Statistically estimated Accuracy based on Theory (StAT) is derived, which numerically quantifies the statistical limitations inherent in microscopic SIP. A state-of-the-art approach to microscopic SIP, namely, the use of automatic speech recognition (ASR) to directly predict listening test results, is evaluated within this framework. The practical results are in good agreement with the theory. As the final contribution, a fully blind DIscriminative Speech intelligibility Predictor (DISP) is introduced and is also evaluated within the StAT framework. It is shown that this novel, blind estimator can predict intelligibility as well as-and often even with better accuracy than-the non-blind ASR-based approach, and that its results are again in good agreement with its theoretically derived performance potential.

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.