Читать книгу The Concise Encyclopedia of Applied Linguistics - Carol A. Chapelle - Страница 219

Historically, many studies have examined whether ASR systems can identify pronunciation errors in non‐native speech and give feedback that can help learners and teachers know what areas of foreign‐language pronunciation are most important for intelligibility. Dalby and Kewley‐Port (1999) demonstrated that such diagnosis and assessment is possible (to some extent) for minimal pairs, and that automatic ratings of pronunciation accuracy can correlate with human ratings. However, the kind of feedback given to learners is not usually very helpful. For those systems that attempt to do so, there are two options: giving a global pronunciation rating or identifying specific errors. To reach either of these goals, ASR systems need to identify word boundaries, accurately align speech to intended targets, and compare the segments produced with those that should have been produced. A variety of systems have been designed to provide global evaluations of pronunciation using automatic measures including speech rate, duration, and spectral analyses (e.g., Neumeyer, Franco, Digalakis, & Weintraub, 2000; Witt & Young, 2000). All of the studies have found that automatic measures are never as good as human ratings, but a combination of automatic measures is always better than a single rating.

ASR systems also have trouble precisely identifying specific errors in articulation, sometimes identifying correct speech as containing errors, but not identifying errors that actually occur. Neri, Cucchiarini, Strik, and Boves (2002) found that only 25% of pronunciation errors were detected by their ASR system, while some correct productions were identified as errors. Truong, Neri, de Wet, Cucchiarini, and Strik (2005) studied whether an ASR system could identify mispronunciations of three sounds typically mispronounced by learners of Dutch. Errors were successfully detected for one of the three sounds, but the ASR system was less successful for the other sounds. However, even modest success in error detection has led to a reduced number of pronunciation errors in comparison to a control group (Cucchiarini, Neri, & Strik, 2009).