On mutual information as a measure of speech intelligibility

Abstract

Speech intelligibility prediction of noisy and processed noisy speech is important in a number of application domains such as hearing instruments and forensics. Most available objective intelligibility measures employ either a signal-to-noise ratio (SNR)-based or correlation-based comparison between frequency bands of the clean and the processed speech. In this paper, we approach the speech intelligibility prediction from the angle of information theory and show that an information theoretic concept provides a unified viewpoint on both the SNR and the correlation based approaches. Two objective intelligibility measures are introduced based on estimated mutual information between the clean speech and the processed speech in the time and the frequency subband domain. Our proposed measures show high correlation with subjective intelligibility measure (i.e. word correct scores) and comparative results with the short-term objective intelligibility measure (STOI).

A weighted STOI intelligibility metric based on mutual information

“…The metric proposed in [23], which estimates mutual information using a k-nearest neighbor estimator, achieved comparative results to STOI in one of the tested performance measures and marginally worse results in the other. The metric in [14], which is computed from the lower bounds of mutual information, achieved a performance approximately equal to that of STOI.…”

“…More recently, metrics based on the mutual information between the spectral envelopes of the clean and degraded signal have been proposed [14,23]. The metric proposed in [23], which estimates mutual information using a k-nearest neighbor estimator, achieved comparative results to STOI in one of the tested performance measures and marginally worse results in the other.…”

“…To obtain the final metric in [22,14,23], the intermediate intelligibility measures computed in different TimeFrequency (TF) regions are averaged using uniform weights. However, it is known that not all portions of a speech signal contain equal quantities of the information required for intelligibility.…”

Objective Intelligibility Measures Based on Mutual Information for Speech Subjected to Speech Enhancement Processing

“…Date of publication September 16, 2013; date of current version November 13, 2013. This paper is an extended version of [1] presented at ICASSP2012, and [2] presented at the ITG Speech Communication Confer-ence2012. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Wai-Yip Geoffrey Chan.…”

Jointly optimal near-end and far-end multi-microphone speech intelligibility enhancement based on mutual information

“…Other metrics that have recently been used to optimize the intelligibility of speech in noise are [13,[21][22][23] Another approach to quantifying speech intelligibility is to use information theory to describe the amount of information that can be transmitted through a speech communication channel. Examples of speech intelligibility predictors based on mutual information (MI) can be found in [24][25][26][27]. In [27] an effective model of human communication based on MI was derived.…”