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

Authors:
C. H. Taal;R. C. Hendriks;R. Heusdens;J. Jensen
Affiliations:
Signal Inf. & Process. Lab., Delft Univ. of Technol., Delft, Netherlands;-;-;-
Venue:
IEEE Transactions on Audio, Speech, and Language Processing
Year:
2011

Citing 0
Cited 6

A Hilbert-fine-structure-derived physical metric for predicting the intelligibility of noise-distorted and noise-suppressed speech

Speech Communication
Unsupervised learning of phonemes of whispered speech in a noisy environment based on convolutive non-negative matrix factorization

Information Sciences: an International Journal
Objective Intelligibility Measures Based on Mutual Information for Speech Subjected to Speech Enhancement Processing

IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Multizone Speech Reinforcement

IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Distributed Delay and Sum Beamformer for Speech Enhancement via Randomized Gossip

IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Speech Intelligibility Prediction Based on Mutual Information

IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)

Quantified Score

Hi-index	0.00

Visualization

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.