Feedback on pronunciation is vital for spoken language teaching. Automatic pronunciation evaluation and feedback can help non-native speakers to identify their errors, learn sounds and vocabulary, and improve their pronunciation performance. Such speech recognition can be performed using Sphinx trained on a database of native exemplar pronunciation and non-native examples of frequent mistakes. ...

In spite of difficulty in defining the syllable unequivocally, and controversy over its role in theories of spoken and written language processing, the syllable is a potentially useful unit in several practical tasks which arise in computational linguistics and speech technology. For instance, syllable structure might embody valuable information for building word models in automatic speech reco...

A novel approach to automatically extracting transliterated-term pairs from Web corpora is proposed in this paper. One of the most important issues addressed is that of taking pronunciation variation into account. Pronunciation variation is a phenomenon of pronunciation ambiguity that seriously affects the term transliteration and hence affects those results produced by transliteration processe...

This paper presents different methods of handling pronunciation variations in Standard Malay (SM) speech recognition. Pronunciation variation can be handled by explicitly modifying the knowledge sources or improving the decoding method. Two types of pronunciation variations are defined, namely, complete or phone changes and partial or sound changes. Complete or phone change means that one phone...

Heterophones pose challenges during training of automatic speech recognition (ASR) systems because they involve ambiguity in the pronunciation of an orthographic representation of a word. Heterophones are words that have the same spelling but different pronunciations. This paper addresses the problem of heterophonic languages by developing the concept of a Composite Phoneme (CP) as a basic pron...

Articulatory feature-based conditional pronunciation modeling (AFCPM) aims to capture the pronunciation characteristics of speakers by modeling the linkage between the states of articulation during speech production and the actual phones produced by a speaker. Previous AFCPM systems use one discrete density function for each phoneme to model the pronunciation characteristics of speakers. This p...

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...

In the process of learning any languages, pronunciation is an important part language acquisition because may influence a listener’s comprehension one’s expression. An example classical mistake pronouncing “sink” and “think.” Because Germans cannot pronounce /s/, rescue worker understand why man who “thinking” needs help. China, English learners are also facing problems pronunciation. Gimson, f...