The subject of this paper is a rule corpus of approx.1500 phonetic rules that models segmental variation of pronunciation in German connected speech. The phonetic rules express on a broad-phonetic level phenomena of phonetic reduction in German that occur within words and across word boundaries The rule corpus has been designed as a component of the Munich AUtomatic Segmentation System (MAUS), ...

computer-assisted language learning (call) is reaching an up most position in the pedagogical field of english as a second or foreign language (esl/efl). the present study was carried out to study the effect of using phonetic websites on iranian efl students’ pronunciation and knowledge of phonemic symbols. participants of the study included 30 efl female pre-intermediate students studying in k...

One of the biggest challenges in designing computer assisted language learning (CALL) applications that provide automatic feedback on pronunciation errors consists in reliably detecting the pronunciation errors at such a detailed level that the information provided can be useful to learners. In our research we investigate pronunciation errors frequently made by foreigners learning Dutch as a se...

Reading is essential to develop while learning English as a foreign language. Thus, this study focuses on aiding children enhance reading by first teaching them letter sounds instead of names. The goal teach how recognize and pronounce individual letters; if they can do so, combine those with reading. Additionally, mitigate the interference from sound L1 L2 in some words. method used answer res...

In the area of Computer Assisted Language Learning(CALL), second language (L2) learners’ spoken data is an important resource for analysing and annotating typical L2 pronunciation errors. The annotation of L2 pronunciation errors in spoken data is not an easy task though, normally it requires manual annotation from trained linguists or phoneticians. In order to facilitate this task, in this pap...

This study focuses on word pronunciation in Text-to-Speech systems for Swedish. The purpose is to investigate whether machine learning techniques match knowledge-based systems in Swedish word pronunciation. The experiments show a maximum grapheme accuracy of just over 97%, and word accuracies from 67.0% for word pronunciation excluding stress assignment, which compares favourably to existing kn...

We present experimental results concerning the application of the IGTree decision-tree learning algorithm to Dutch word pronunciation. We evaluate four diierent Dutch word pronunciation systems conngured to test the utility of modularization of grapheme{to{phoneme transcription (G) and stress prediction (S). Both training and testing data are extracted from the CELEX II lexical database. Experi...

An automatic transcription system has been developed to label and segment phonetic constituents of spontaneous American English without benefit of a word-level transcript. Instead, special-purpose neural networks classify each 10-ms frame of speech in terms of articulatory-acoustic-based phonetic features and the feature clusters are subsequently mapped to phonetic-segment labels using multilay...

In this paper the application of automatic speech recognition (ASR) technology in CAPT (Computer Aided Pronunciation Training) is addressed. A method to automatically generate the competitive lexicon, required by an ASR engine to compare the pronunciation of a target word with its correct and wrong phonetic realization, is presented. In order to enable the efficient deployment of CAPT applicati...

Building on previous works (e.g. Kubozono 2006, and Kang 2010), this article attempts to establish a taxonomy for loanword prosody, referring specifically to the patterns of stress, tone, or pitch-accent that are found in loanwords. Toward a taxonomy, we consider the following factors: (i) whether the pronunciation of the word in the source language influences the assignment of prosody in the b...