By Topic

Performance Estimation of Speech Recognition System Under Noise Conditions Using Objective Quality Measures and Artificial Voice

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
T. Yamada ; Graduate Sch. of Syst. & Inf. Eng., Tsukuba Univ. ; M. Kumakura ; N. Kitawaki

It is essential to ensure quality of service (QoS) when offering a speech recognition service for use in noisy environments. This means that the recognition performance in the target noise environment must be investigated. One approach is to estimate the recognition performance from a distortion value, which represents the difference between noisy speech and its original clean version. Previously, estimation methods using the segmental signal-to-noise ratio (SNRseg), the cepstral distance (CD), and the perceptual evaluation of speech quality (PESQ) have been proposed. However, their estimation accuracy has not been verified for the case when a noise reduction algorithm is adopted as a preprocessing stage in speech recognition. We, therefore, evaluated the effectiveness of these distortion measures by experiments using the AURORA-2J connected digit recognition task and four different noise reduction algorithms. The results showed that in each case the distortion measure correlates well with the word accuracy when the estimators used are optimized for each individual noise reduction algorithm. In addition, it was confirmed that when a single estimator, optimized for all the noise reduction algorithms, is used, the PESQ method gives a more accurate estimate than SNRseg and CD. Furthermore, we have proposed the use of artificial voice of several seconds duration instead of a large amount of real speech and confirmed that a relatively accurate estimate can be obtained by using the artificial voice

Published in:

IEEE Transactions on Audio, Speech, and Language Processing  (Volume:14 ,  Issue: 6 )