The pronunciation variations, which badly influenced the performance of ASR system, are serious in continuous speech, especially in spontaneous speech. Many research works are focused on pronunciation variation modeling in recent years. A framework for Chinese pronunciation variation modeling is described in this paper. The main idea is that the pronunciation variations are hidden in the recogn...

1. Introduction This study aims at developing an automated pronunciation scoring method for second language learners of English (Hereafter, L2 learners) using both confidence scoring and classifiers. The pronunciation errors have been detected using the confidence measure from speech recognition [Franco et al. However, the accuracy of the assessment based on the confidence scores is not always ...

We propose an e ective application of speech recognition to foreign language pronunciation learning. The objective of our system is to detect pronunciation errors and provide diagnostic feedback through speech processing and recognition methods. Automatic pronunciation error detection is used for two kinds of mispronunciation, that is mistake and linguistical inheritance. The correlation betwee...

This paper presents results of a socio-phonetic analysis of Taiwan Mandarin by using a corpus of questionnaire-based interview speech. Questions were asked to collect data of the interviewee’s background of language use, socio-economic status, and internet access in different regions of Taiwan. Two typical dialect-influenced pronunciation errors, the deletion of /w/ before /o/ and the delabilia...

This paper presents a new method for the verification of the correct pronunciation of spoken words. This process is based on speech recognition technology. It can be particularly useful when applied to the field of SLA (Second Language Acquisition) in learning environments or Computer-Aided Language Learning (CALL) systems, where the students can practice their pronunciation skills. This method...

We have tested a fixed version of the Fluency foreign language pronunciation trainer to determine whether users actually improve their pronunciation through its use. The Fluency system uses the CMU SPHINX II speech recognizer to pinpoint pronunciation errors and then gives suggestions as to how to correct them. Users in the experiment we will describe in this paper were to learn to pronounce th...

In this paper we report on a study that was carried out to obtain an inventory of segmental errors in the Dutch of adult learners with different mother tongues (L1s). The errors observed were subsequently examined in detail to select a number of errors that should receive priority in Computer Assisted Pronunciation Training (CAPT) for Dutch as L2.

Pronunciation dictionaries are the interface between orthographic and phonetic representation of the speech signal and are thereby a substantial component of speech recognition systems. In many systems simple canonical pronunciation forms are used within the dictionary. They represent the “correct” pronunciation as they are found in lexicons and neither contain the most frequent pronunciation n...

Improving the feedback quality of a computer-based system for pronunciation training requires rather detailed and precise knowledge about the place and the nature of actual mispronunciations in a student’s utterance. To be able to provide this kind of information, components for the automatic localisation and correction of pronunciation errors have been developed. This work was part of a projec...

Improving the feedback quality of a computer-based system for pronunciation training requires rather detailed and precise knowledge about the place and the nature of actual mispronunciations in a student’s utterance. To be able to provide this kind of information, components for the automatic localization and correction of pronunciation errors have been developed. This work was part of a projec...