Читать книгу The Concise Encyclopedia of Applied Linguistics - Carol A. Chapelle - Страница 218

ASR has tremendous potential in applied linguistics. In one application area, that of language teaching, Eskenazi (1999) compares the strengths of ASR to effective immersion language learning in developing spoken‐language skills. ASR‐based systems can provide a way for learners of a foreign language to hear large amounts of the foreign language spoken by many different speakers, produce speech in large amounts, and get relevant feedback. In addition, Eskenazi (1999) suggests that using ASR computer‐assisted language learning (CALL) materials allows learners to feel at greater ease and get more consistent assessment of their skills. ASR can also be used for virtual dialogues with native speakers (Harless, Zier, & Duncan, 1999) and for pronunciation training (Dalby & Kewley‐Port, 1999). Most importantly, learners enjoy ASR applications. Study after study indicates that appropriately designed software that includes ASR is a benefit to language learners in terms of practice, motivation, and the feeling that they are actually communicating in the language rather than simply repeating predigested words and sentences.

The holy grail of a computer recognition system that matches human speech recognition remains out of reach at present. A number of limitations appear consistently in attempts to apply ASR systems to foreign language‐learning contexts. The major limitation occurs because most ASR systems are designed to work with a limited range of native speech patterns. Consequently, most ASR systems do not do well in recognizing non‐native speech, both because of unexpected phone mapping and because of prosody differences. In one now dated study, Derwing, Munro, and Carbonaro (2000) tested Dragon Naturally Speaking's ability to identify errors in speech of very advanced L2 speakers of English. Human listeners were able to successfully transcribe between 95% and 99.7% of the words, and the recognition rates by the program were a respectable 90% for native English speakers. In contrast, the system accurately transcribed only around 70% for the non‐native speakers who were mostly intelligible to human listeners. Despite problems with L2 speech recognition, recent studies have demonstrated that even imperfect commercial recognizers can be helpful in providing feedback on pronunciation (McCrocklin, 2016; Liakin, Cardoso, & Liakina, 2017).

In addition, ASR systems have been built for word recognition rather than assessment and feedback, and thus many commercial recognition systems offer only implicit feedback on pronunciation but not specific mispronunciation detection. However, most language learners require assessment of the specifics of their pronunciation and specific feedback to make progress. Fortunately, these are topics that are consistently being explored in speech sciences (e.g., Duan, Kawahara, Dantsuji, & Zhang, 2017).