Carol A. Chapelle - The Concise Encyclopedia of Applied Linguistics

There are other areas in which ASR has been used by applied linguists: reading instruction and the use of ASR in dialogue systems used with language‐learning software. One use of ASR that seems to have been particularly successful has been in teaching children to read. Mostow and Aist (1999) found that ASR used in conjunction with an understanding of teacher–student classroom behavior was successful in teaching oral reading skills and word recognition. In a later study, Poulsen, Hastings, and Allbritton (2007) found that reading interventions for young learners of English were far more effective when an ASR system was included.

Another use of ASR technology is in spoken CALL dialogue systems. If a software program for practicing spoken language provides the first line of a dialogue, learners give one of two responses. If these responses are dissimilar, the ASR system can recognize which sentence has been spoken (even with pronunciation errors or missing words). The computer can then respond, allowing the learner to respond again from a menu of possible responses (see Bernstein, Najmi, & Ehsani, 1999; Harless et al., 1999). O'Brien (2006) reviews a number of such programs. With the recent advent of deep learning systems, which take advantage of established techniques for modeling phone recognition and for making use of spectral information from speech, it is likely that machine feedback will soon become more accurate and flexible in L2 speech recognition.

Automatic speech recognition holds great promise for applied linguistics. ASR research and usability testing is happening in areas likely to impact applied linguistics (e.g., Anderson, Davidson, Morton, & Jack, 2008). For example, the International Conference on Acoustics, Speech and Signal Processing (ICASSP); the annual INTERSPEECH conference (held through the International Speech Communication Association, or ISCA); and the ISCA Special Interest Group on Speech and Language Technology in Education (SlaTE; see http://hstrik.ruhosting.nl/slate/) bring together those working in areas that will eventually influence linguistic applications.

The connections between ASR and text‐to‐speech software have been insufficiently explored in applied linguistic circles, but both are regularly examined in cutting‐edge work tied to other areas of speech sciences. We expect that the ubiquity of mobile devices that use ASR‐based applications will eventually allow L2 learners to practice their L2 speaking skills and receive effective feedback on their pronunciation. Further progress in ASR will likely result in interactive language‐learning systems capable of providing authentic interaction opportunities with real or virtual interlocutors. These systems will also become able to produce specific, corrective feedback to learners on their pronunciation errors. Additionally, the development of noise‐robust ASR technologies will allow language learners to use ASR‐based products in noise‐prone environments such as classrooms, transportation, and other public places. Finally, the performance of ASR systems will improve as emotion recognition and visual speech recognition (based, for instance, on a Webcam's capturing of learners' lip movements and facial expressions) become more effective and widespread.

SEE ALSO:Computer‐Assisted Pronunciation Teaching; Foreign Accent; Innovation in Language Teaching and Learning; Pronunciation Assessment; Pronunciation Teaching Methods and Techniques

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