We present in this paper a statistical model for languageindependent bi-directional conversion between spelling and pronunciation, based on joint grapheme/phoneme units extracted from automatically aligned data. The model is evaluated on spelling-to-pronunciation and pronunciation-tospelling conversion on the NetTalk database and the CMU dictionary. We also study the effect of including lexical...

We propose a model-driven method for ensuring the quality of pronunciation dictionaries. The key ingredient is computing an alignment between letter strings and phoneme strings, a standard technique in pronunciation modeling. The novel aspect of our method is the use of informative, parametric alignment models which are refined iteratively as they are tested against the data. We discuss the use...

This paper presents the design and performance of a Malay grapheme to phoneme (G2P) tool for generating the pronunciation dictionary for a Malay automatic speech recognition system (ASR). The G2P tool is a rule based system. It is flexible in adding and removing rules, and handling of English words. The G2P tool also contains morphological and syllable tool, which it uses to determine the pronu...

This paper describes the implementation of a full-scale pronunciation lexicon for Turkish using finite state technology. The system produces at its output, a parallel representation of the pronunciation and the morphological analysis of the word form so that morphological disambiguation can be used to disambiguate pronunciation. The pronunciation representation is based on the SAMPA standard an...

2 Individual Pronunciation Coaching The difficulties experienced while learning the pronunciation of a second language do not only depend on the mother tongue of the learners but also on individual differences between learners. Even students of the same mother tongue and with a very similar learning background show huge differences in their individual pronunciation. Learners also differ in term...

In this paper, a data-driven approach to statistical modeling pronunciation variation is proposed. It consists of learning stochastic pronunciation rules. The proposed method jointly models different rules that define the same transformation. Hierarchic Grouping Rule Inference (HIEGRI) algorithm is proposed to generate this model based on graphs. HIEGRI algorithm detects the common patterns of ...

In order to help understand why gains in pronunciation modeling have proven so elusive, we investigated which kinds of pronunciation variation are well captured by triphone models, and which are not. We do this by examining the change in behavior of a recognizer as it receives further triphone training. We show that many of the kinds of variation which previous pronunciation models attempt to c...

Articulatory information can support learning or remediating pronunciation of a second language (L2). This paper describes an electromagnetic articulometer-based visual-feedback approach using an articulatory target presented in real-time to facilitate L2 pronunciation learning. This approach trains learners to adjust articulatory positions to match targets for a L2 vowel estimated from product...

In this paper, we propose speaker-dependent multiple-pronunciation lexical modeling for improving the performance of dysarthric automatic speech recognition (ASR). For each dysarthric speaker, a phoneme confusion matrix is first constructed from the results of phoneme recognition. Then, pronunciation variation rules are extracted by investigating the phoneme confusion matrix, and they are incor...

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...