Regular English Pronunciation (REP) is an artificial accent of English designed to be more logically related to English spelling than modern naturally-occurring English accents. The REP pronunciation of words can be generated automatically with a set of just 200 rules and exceptions. These rules and exceptions have been measured to provide over 75% of standard pronunciations in running spoken E...

In recent years, there are a considerable number of new immigrants in Taiwan. Although these people are in the good position to learn Chinese, the advantages are limited to speaking and listening. Recognizing Chinese characters is a tough task since one has to memorize the shape, meaning and pronunciation at the same time. Therefore, the cost of learning a single character is relatively high co...

In recent years, Computer Assisted Pronunciation Technology (CAPT) systems have been developed that can help Japanese learners to study foreign languages. We have been developing a pronunciation training system to evaluate and correct learner's pronunciation by extracting articulatory-features (AFs). In this paper, we propose a novel pronunciation training system that can plot the place and man...

In this paper, we focus on the modeling of coarticulation and pronunciation variation in Automatic Speech Recognition systems (ASR). Most ASR systems explicitly describe these production phenomena through context-dependent phoneme models and multiple pronunciation lexicons. Here, we explore the potential benefit of using feature spaces covering longer time segments in terms of implicit modeling...

Objective evaluation allows a model to be compared with other similar models. However, automatic pronunciation models should also be extensively evaluated by humans, since the ultimate goal of any pronunciation model is to produce an accurate pronunciation as judged by most people. This paper describes an initiative to evaluate and collect proper name pronunciations online, the development of t...

This work presents a general development framework for automatic pronunciation assessment within computer-assisted language learning (CALL) together with several reenements of a previously described pronunciation scoring method. This method utilises a likelihood-based`Goodness of Pronunciation' (GOP) measure which in this work has been extended to include individual thresholds for each phone ba...

We propose a stochastic pronunciation model using an ergodic hidden Markov model (EHMM) of automatically derived acoustic sub-word units (SWU). The proposed EHMM discovers the pronunciation structure inherent in the acoustic training data of a word without any apriori phonetic transcriptions. The EHMM is an HMM of HMMs – its states are SWU HMMs and the state-transitions compose various possible...

In this paper we measure the degrees of association among aggregate pronunciational, lexical and syntactic differences in 70 Dutch dialect varieties. First, we show that pronunciation is marginally more strongly associated with syntax than it is with lexis and that syntax and lexis are only weakly associated. Then, we check for the influence of geography as an underlying factor because geograph...

A significant source of variation in spontaneous speech is due to intra-speaker pronunciation changes. Previous work in automatic speech recognition has identified several factors that affect pronunciation variability such as phonetic context and speaking rate, as well as syntactic structure. This work examines prosody as a cue to pronunciation variability, as represented by attributes derived ...