Discriminative Phoneme Sequences Extraction for Non-Native Speaker's Origin Classification
Identifieur interne : 004B50 ( Main/Merge ); précédent : 004B49; suivant : 004B51Discriminative Phoneme Sequences Extraction for Non-Native Speaker's Origin Classification
Auteurs : Ghazi Bouselmi [France] ; Dominique Fohr [France] ; Irina Illina [France] ; Jean-Paul Haton [France]Source :
Descripteurs français
Abstract
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 method is based on the extraction of discriminative sequences of phonemes from a non-native English speech database. These sequences are used to construct a probabilistic classifier for the speakers' origin. The existence of discriminative phone sequences in non-native speech is a significant result of this work. The system that we have developed achieved a significant correct classification rate of 96.3% and a significant error reduction compared to some other tested techniques.
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<front><div type="abstract" xml:lang="en">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 method is based on the extraction of discriminative sequences of phonemes from a non-native English speech database. These sequences are used to construct a probabilistic classifier for the speakers' origin. The existence of discriminative phone sequences in non-native speech is a significant result of this work. The system that we have developed achieved a significant correct classification rate of 96.3% and a significant error reduction compared to some other tested techniques.</div>
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