An Algorithm for Intelligibility Prediction of Time-Frequency Weighted Noisy Speech

• Published in: IEEE transactions on audio, speech, and language processing Vol. 19; no. 7; pp. 2125 - 2136
• Main Authors: Taal, C. H., Hendriks, R. C., Heusdens, R., Jensen, J.
• Format: Journal Article
• Language: English
• Published: Piscataway, NJ IEEE 01.09.2011; Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Applied sciences; Correlation; Correlation analysis; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Information, signal and communications theory; Intelligibility; Matlab; Noise measurement; Noise reduction; objective measure; Segments; Sentences; Signal and communications theory; Signal processing; Signal to noise ratio; Signal, noise; Speech; speech enhancement; speech intelligibility prediction; Speech processing; Statistics; Telecommunications and information theory; Time frequency analysis; Speech analysis; Noise reduction; Objective analysis; Speech intelligibility; Verbal perception; objective measure; Algorithm; Implementation; Reference model; Speech enhancement; Signal processing; speech intelligibility prediction; Sentence; Speech processing
• ISSN: 1558-7916; 1558-7924; 1558-7924
• DOI: 10.1109/TASL.2011.2114881

In the development process of noise-reduction algorithms, an objective machine-driven intelligibility measure which shows high correlation with speech intelligibility is of great interest. Besides reducing time and costs compared to real listening experiments, an objective intelligibility measure could also help provide answers on how to improve the intelligibility of noisy unprocessed speech. In this paper, a short-time objective intelligibility measure (STOI) is presented, which shows high correlation with the intelligibility of noisy and time-frequency weighted noisy speech (e.g., resulting from noise reduction) of three different listening experiments. In general, STOI showed better correlation with speech intelligibility compared to five other reference objective intelligibility models. In contrast to other conventional intelligibility models which tend to rely on global statistics across entire sentences, STOI is based on shorter time segments (386 ms). Experiments indeed show that it is beneficial to take segment lengths of this order into account. In addition, a free Matlab implementation is provided.