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...

This paper presents a mispronunciation detection system which uses automatic speech recognition to support computer-aided pronunciation training (CAPT). Our methodology extends a model pronunciation lexicon with possible phonetic mispronunciations that may appear in learners’ speech. Generation of these pronunciation variants was previously achieved by means of phone-tophone mapping rules deriv...

There are obvious issues with regard to teaching pronunciation to Gujarati Speakers of English (GSE). These issues have its root in the inherent difference between the phonology of Gujarati and English at both segmental and supra-segmental level. There is sufficient research evidence to prove that these phonological differences are largely responsible for problems in teaching English pronunciat...

The Default&Refine algorithm is a new rule-based learning algorithm that was developed as an accurate and efficient pronunciation prediction mechanism for speech processing systems. The algorithm exhibits a number of attractive properties including rapid generalisation from small training sets, good asymptotic accuracy, robustness to noise in the training data, and the production of compact rul...

Conversational speech exhibits considerable pronunciation variability, which has been shown to have a detrimental effect on the accuracy of automatic speech recognition. There have been many attempts to model pronunciation variation, including the use of decision-trees to generate alternate word pronunciations from phonemic baseforms. Use of such pronunciation models during recognition is known...

The present paper investigates multiword expressions (MWEs) in spo­ ken language and possible ways of identifying MWEs automatically in speech corpora. Two MWEs that emerged from previous studies and that occur frequently in Dutch are analyzed to study their pronunciation characteristics and compare them to those of other utterances in a large speech corpus. The analyses reveal that these MWEs ...

A tool for automatic pronunciation evaluation of singing is desirable for those learning a second language. However, efforts to obtain pronunciation rules for such a tool have been hindered by a lack of data; while many spokenword datasets exist that can be used in developing the tool, there are relatively few sung-lyrics datasets for such a purpose. In this paper, we demonstrate a proof-of-pri...

10 In this study we develop pronunciation distances based on naive discriminative learning (NDL). 11 Measures of pronunciation distance are used in several subfields of linguistics, including 12 psycholinguistics, dialectology and typology. In contrast to the commonly used Levenshtein algorithm, 13 NDL is grounded in cognitive theory of competitive reinforcement learning and is able to generate...

The pronunciation lexicon is a fundamental element in an automatic speech transcription system. It associates each lexical entry (usually a grapheme), with one or more phonemic or phone-like forms, the pronunciation variants. Thorough knowledge of the target language is a priori necessary to establish the pronunciation baseforms and variants. The reliance on human expertise can pose difficultie...

Most phonetic learner corpora are dictionary-based and transcribe a target / pronunciation model for learners [1], which is usually an American pronunciation model, because the electronic resources for British English are scarce. This paper proposes to investigate which reference pronunciation model learners aim to emulate, by comparing their realisations with two reference formant datasets, wh...