Non-Native Pronunciation Variation Modeling for Automatic Speech Recognition

Abstract

Communication using speech is inherently natural, with this ability of communication unconsciously acquired in a step-by-step manner throughout life. In order to explore the benefits of speech communication in devices, there have been many research works performed over the past several decades. As a result, automatic speech recognition (ASR) systems have been deployed in a range of applications, including automatic reservation systems, dictation systems, navigation systems, etc. Due to increasing globalization, the need for effective interlingual communication has also been growing. However, because of the fact that most people tend to speak foreign languages with variant or influent pronunciations, this has led to an increasing demand for the development of non-native ASR systems (Goronzy et al., 2001). In other words, a conventional ASR system is optimized with native speech; however, non-native speech has different characteristics from native speech. That is, non-native speech tends to reflect the pronunciations or syntactic characteristics of the mother tongue of the non-native speakers, as well as the wide range of fluencies among non-native speakers. Therefore, the performance of an ASR system evaluated using non-native speech tends to severely degrade when compared to that of native speech due to the mismatch between the native training data and the nonnative test data (Compernolle, 2001). A simple way to improve the performance of an ASR system for non-native speech would be to train the ASR system using a non-native speech database, though in reality the number of non-native speech samples available for this task is not currently sufficient to train an ASR system. Thus, techniques for improving non-native ASR performance using only small amount of non-native speech are required. There have been three major approaches for handling non-native speech for ASR: acoustic modeling, language modeling, and pronunciation modeling approaches. First, acoustic modeling approaches find pronunciation differences and transform and/or adapt acoustic models to include the effects of non-native speech (Gruhn et al., 2004; Morgan, 2004; Steidl et al., 2004). Second, language modeling approaches deal with the grammatical effects or speaking style of non-native speech (Bellegarda, 2001). Third, pronunciation modeling approaches derive pronunciation variant rules from non-native speech and apply the derived rules to pronunciation models for non-native speech (Amdal et al., 2000; FoslerLussier, 1999; Goronzy et al., 2004; Gruhn et al., 2004; Raux, 2004; Strik et al., 1999). Source: Advances in Speech Recognition, Book edited by: Noam R. Shabtai, ISBN 978-953-307-097-1, pp. 164, September 2010, Sciyo, Croatia, downloaded from SCIYO.COM