Computer-assisted language learning (CALL) provides an effective learning environment so that students can practice in an interactive manner using multi-media content, either with the supervision of teachers or on their own pace in self-learning. The advancement of speech and language technologies has opened new perspectives on CALL systems, such as automatic pronunciation assessment and simulated conversational-style lessons. CALL is also regarded as one of new and promising applications of speech analysis, recognition and synthesis. CALL covers a variety of aspects including segmental, prosodic and lexical features. Modeling non-native speech to correctly segment/recognize utterances while detecting errors included in them poses a number of challenges in speech processing. Assessing intelligibility of non-native speech or proficiency of non-native speakers is also an important issue. In this tutorial, we will give an overview on these issues and current solutions. The tutorial is mainly targeted for speech researchers and engineers interested in CALL, but also for those engaged in language teaching or learning technology.
First we review speech recognition technologies for pronunciation learning, specifically pronunciation evaluation and error detection. Statistical approaches to these problems are formulated, and then acoustic and pronunciation modeling of non-native speech is described. Unlike the conventional non-native speech recognition, error detection capability is required in CALL, thus an effective error prediction scheme is vitally important. Next, we address prosodic modeling and evaluation, such as duration, stress and tones, and then the use of speech synthesis technologies including re-synthesis and morphing.
After the review of basic component technologies, we introduce a number of practical CALL systems which have been developed as commercial products or deployed in classrooms, including those in our universities. Majority of them focus on learning English as a second language (ESL), but some deal with other languages such as Japanese and Chinese. We also review databases of non-native speech, which are necessary to develop CALL systems.
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