Non-native speakers often face difficulty in pronouncing like the native speakers. This paper proposes to model pronunciation variation in non-native speaker’s speech using only acoustics models, without the need for the corpus. Variation in term of context and language will be modeled. The combination of both modeling resulted in the reduction of absolute WER as much as 16% and 6% for native V...

Speech pauses between turns of conversations are crucial for assessing conversation partners’ cognitive states, such as their knowledge, confidence and willingness to grant requests; in general, speakers making longer regarded less apt willing. However, it is unclear if the interpretation pause length mediated by accent interactants, particular native versus non-native accents. We hypothesized ...

In this paper we present an automated method for the classification of the origin of non-native speakers. The origin of non-native speakers could be identified by a human listener based on the detection of typical pronunciations for each nationality. Thus we suppose the existence of several phoneme sequences that might allow the classification of the origin of non-native speakers. Our new metho...

In this paper we present an automated method for the classification of the origin of non-native speakers. The origin of non-native speakers could be identified by a human listener based on the detection of typical pronunciations for each nationality. Thus we suppose the existence of several phoneme sequences that might allow the classification of the origin of non-native speakers. Our new metho...

With more and more non-native speakers speaking in English, the fast and efficient adaptation to non-native English speech becomes a practical concern. The performance of speech recognition systems is consistently poor on non-native speech. The challenge for non-native speech recognition is to maximize the recognition performance with small amount of non-native data available. In this paper we ...

A speech recognition system is subjected to the speech of non-native speakers, using both native and non-native acoustic phone models. The problems involved with the mapping of phoneset from the nonnative to native language are investigated, and a detailed analysis of phone confusions is made. For Dutch speakers, British English acoustic models give the best word recognition results.

In order to improve automatic recognition of English commands spoken by non-native speakers, we have modeled non-native pronunciation variation of Dutch, French and Italian. The results of lexical and acoustical modeling appeared to be source language and speaker dependent. Lexical modeling only resulted in a substantial improvement (of 35%) for the French speakers. Acoustic model adaptation ha...