An inventory was compiled of pronunciation errors frequently made by foreigners speaking Dutch. On the basis of this inventory artificial errors were created in a native development corpus, which in turn were used to optimize thresholds for the Goodness of Pronunciation (GOP) algorithm. In the current study the GOP algorithm is evaluated in three different ways: (1) using a native test corpus w...

An inventory was compiled of pronunciation errors frequently made by foreigners speaking Dutch. On the basis of this inventory artificial errors were created in a native development corpus, which in turn were used to optimize thresholds for the Goodness of Pronunciation (GOP) algorithm. In the current study the GOP algorithm is evaluated in three different ways: (1) using a native test corpus w...

An inventory was compiled of pronunciation errors frequently made by foreigners speaking Dutch. On the basis of this inventory artificial errors were created in a native development corpus, which in turn were used to optimize thresholds for the Goodness of Pronunciation (GOP) algorithm. In the current study the GOP algorithm is evaluated in three different ways: (1) using a native test corpus w...

English is the only language available for global communication. Due to the influence of speakers’ mother tongue, however, those from different regions often have different accents in their pronunciation of English. The ultimate goal of our project is automatic creation of a global pronunciation map of World Englishes on an individual basis, for speakers to use to locate similar English pronunc...

An inventory was compiled of pronunciation errors frequently made by foreigners speaking Dutch. On the basis of this inventory artificial errors were created in a native development corpus, which in turn were used to optimize thresholds for the Goodness of Pronunciation (GOP) algorithm. In the current study the GOP algorithm is evaluated in three different ways: (1) using a native test corpus w...

Non-native speech differs significantly from native speech, often resulting in a degradation of the performance of automatic speech recognition (ASR). Hand-crafted pronunciation lexicons used in standard ASR systems generally fail to cover non-native pronunciations, and design of new ones by linguistic experts is time consuming and costly. In this work, we propose acoustic data-driven iterative...

In this paper, we address the importance of pronunciation and acoustic model adaptation in multilingual speech recognition. When aiming at modeling several languages simultaneously, the degree of speaker and language variability is even greater than when concentrating on only one language. To compensate the pronunciation variability across various speaker, bi-lingual pronunciation modeling is p...

This paper investigates the features that determine the sentence pronunciation difficulty for Korean speakers of English. We selected three types of features: length, word frequency, and phonemes that Korean speakers generally replace with other phonemes. We used support vector machines and a multiple linear regression model to determine the pronunciation difficulty of given sentences, and meas...

Introducing pronunciation models into decoding has been proven to be benefit to LVCSR. In this paper, a discriminative pronunciation modeling method is presented, within the framework of the Minimum Phone Error (MPE) training for HMM/GMM. In order to bring the pronunciation models into the MPE training, the auxiliary function is rewritten at word level and decomposes into two parts. One is for ...

In this study we present various techniques to evaluate the pronunciation of students of a foreign language without any knowledge of the uttered text. Previous attempts have shown that it is feasible to evaluate the pronunciation of a non-native speaker by having implicit or explicit knowledge of the uttered text, provided that enough utterances are available. Our approach is to use characteris...