An algorithm for intelligibility prediction of time-frequency weighted noisy speech

  • Taal C
  • Hendriks R
  • Heusdens R
 et al. 
  • 150

    Readers

    Mendeley users who have this article in their library.
  • 334

    Citations

    Citations of this article.

Abstract

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

Author-supplied keywords

  • Noise reduction
  • objective measure
  • speech enhancement
  • speech intelligibility prediction

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Get full text

Authors

  • Cees H. Taal

  • Richard C. Hendriks

  • Richard Heusdens

  • Jesper Jensen

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free