Language accent classification in American English
Speech Communication
PLASER: pronunciation learning via automatic speech recognition
HLT-NAACL-EDUC '03 Proceedings of the HLT-NAACL 03 workshop on Building educational applications using natural language processing - Volume 2
Discriminative speaker adaptation using articulatory features
Speech Communication
Articulatory feature recognition using dynamic Bayesian networks
Computer Speech and Language
Towards capturing fine phonetic variation in speech using articulatory features
Speech Communication
Conditional Random Fields for Integrating Local Discriminative Classifiers
IEEE Transactions on Audio, Speech, and Language Processing
Advances in phone-based modeling for automatic accent classification
IEEE Transactions on Audio, Speech, and Language Processing
Using Articulatory Representations to Detect Segmental Errors in Nonnative Pronunciation
IEEE Transactions on Audio, Speech, and Language Processing
Improving mispronunciation detection using adaptive frequency scale
Computers and Electrical Engineering
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The problem of accent analysis and modeling has been considered from a variety of domains, including linguistic structure, statistical analysis of speech production features, and HMM/GMM (hidden Markov model/Gaussian mixture model) model classification. These studies however fail to connect speech production from a temporal perspective through a final classification strategy. Here, a novel accent analysis system and methodology which exploits the power of phonological features (PFs) is presented. The proposed system exploits the knowledge of articulation embedded in phonology by building Markov models (MMs) of PFs extracted from accented speech. The Markov models capture information in the PF space along two dimensions of articulation: PF state-transitions and state-durations. Furthermore, by utilizing MMs of native and non-native accents, a new statistical measure of ''accentedness'' is developed which rates the articulation of a word by a speaker on a scale of native-like (+1) to non-native like (-1). The proposed methodology is then used to perform an automatic cross-sectional study of accented English spoken by native speakers of Mandarin Chinese (N-MC). The experimental results demonstrate the capability of the proposed system to perform quantitative as well as qualitative analysis of foreign accents. The work developed in this study can be easily expanded into language learning systems, and has potential impact in the areas of speaker recognition and ASR (automatic speech recognition).