Providing feedback on pronunciation errors in computer assisted language learning systems requires that pronunciation errors be detected automatically. In the present study we compare four types of classifiers that can be used for this purpose: two acoustic-phonetic classifiers (one of which employs linear-discriminant analysis (LDA)), a classifier based on cepstral coefficients in combination ...

In this paper we report on a study on pronunciation errors by Spanish learners of Dutch, which was aimed at obtaining information to develop a dedicated Computer Assisted Pronunciation Training (CAPT) program for this fixed language pair (Spanish L1, Dutch L2). The results of our study indicate, that, first, vowel errors are more frequent and variable than consonant mispronunciations. Second, S...

In light of the fact that L2 pronunciation errors are often caused by the transfer of well-established L1 sound systems, this paper examines some of the outstanding phonological differences between Persian and English. Comparing segmental and supra-segmental aspects of both languages, this study also discusses several problematic areas of pronunciation facing Iranian learners of English. To rea...

In light of the fact that L2 pronunciation errors are often caused by the transfer of well-established L1 sound systems, this paper examines some of the characteristic phonological differences between Japanese and English. Comparing segmental and suprasegmental aspects of both languages, this study also discusses several problematic areas of pronunciation for Japanese learners of English. Based...

We describe a new language-independent technique for automatically identifying errors in an electronic pronunciation dictionary by analyzing the source of conflicting patterns directly. We evaluate the effectiveness of the technique in two ways: we perform a controlled experiment using artificially corrupted data (allowing us to measure precision and recall exactly); and then apply the techniqu...

An automatic system for detection of pronunciation errors by adult learners of English is embedded in a language–learning package. Four main features are: (1) a recognizer robust to non–native speech; (2) localization of phone– and word–level errors; (3) diagnosis of what sorts of phone–level errors took place; and (4) a lexical– stress detector. These tools together allow robust, consistent, a...

This paper investigates a method of automatic pronunciation scoring for use in computer-assisted language learning (CALL) systems. The method utilises a likelihood-based `Goodness of Pronunciation' (GOP) measure which is extended to include individual thresholds for each phone based on both averaged native con®dence scores and on rejection statistics provided by human judges. Further improvemen...

We describe the methodologies for collecting and annotating a Latin-American Spanish speech database. The database includes recordings by native and nonnative speakers. The nonnative recordings are annotated with ratings of pronunciation quality and detailed phonetic transcriptions. We use the annotated database to investigate rater reliability, the e ect of each phone on overall perceived nonn...