Performance improvement of automatic pronunciation assessment in a noisy classroom

Abstract

In recent years Computer-Assisted Language Learning (CALL) systems have been widely used in foreign language education. Some systems use automatic speech recognition (ASR) technologies to detect pronunciation errors and estimate the proficiency level of individual students. When speech recording is done in a CALL classroom, however, utterances of a student are always recorded with those of the others in the same class. The latter utterances are just background noise, and the performance of automatic pronunciation assessment is degraded especially when a student is surrounded with very active students. To solve this problem, we apply a noise reduction technique, Stereo-based Piecewise Linear Compensation for Environments (SPLICE), and the compensated feature sequences are input to a Goodness Of Pronunciation (GOP) assessment system. Results show that SPLICE-based noise reduction works very well as a means to improve the assessment performance in a noisy classroom.