Abstract

Automatically evaluating pronunciation quality of non-native speech has seen tremendous success in both research and commercial settings, with applications in L2 learning. In this paper, submitted for the INTERSPEECH 2015 Degree of Nativeness Sub-Challenge, this problem is posed under a challenging crosscorpora setting using speech data drawn from multiple speakers from a variety of language backgrounds (L1) reading different English sentences. Since the perception of non-nativeness is realized at the segmental and suprasegmental linguistic levels, we explore a number of acoustic cues at multiple time scales. We experiment with both data-driven and knowledge-inspired features that capture degree of nativeness from pauses in speech, speaking rate, rhythm/stress, and goodness of phone pronunciation. One promising finding is that highly accurate automated assessment can be attained using a …

Date

January 1, 1970

Authors

Matthew P Black, Daniel Bone, Zisis Iason Skordilis, Rahul Gupta, Wei Xia, Pavlos Papadopoulos, Sandeep Nallan Chakravarthula, Bo Xiao, Maarten Van Segbroeck, Jangwon Kim, Panayiotis G Georgiou, Shrikanth S Narayanan

Conference

INTERSPEECH

Pages

493-497