Robust language identification based on fused phonotactic information with MLKSFM pre-classifier
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Improved N-grams approach for web page language identification
Transactions on computational collective intelligence V
Identification of Indian languages using multi-level spectral and prosodic features
International Journal of Speech Technology
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This paper addresses the problem of modeling prosody for language identification. The aim is to create a system that can be used prior to any linguistic work to show if prosodic differences among languages or dialects can be automatically determined. In previous papers, we defined a prosodic unit, the pseudosyllable. Rhythmic modeling has proven the relevance of the pseudosyllable unit for automatic language identification. In this paper, we propose to model the prosodic variations, that is to say model sequences of prosodic units. This is achieved by the separation of phrase and accentual components of intonation. We propose an independent coding of those components on differentiated scales of duration. Short-term and long-term language-dependent sequences of labels are modeled by n-gram models. The performance of the system is demonstrated by experiments on read speech and evaluated by experiments on spontaneous speech. Finally, an experiment is described on the discrimination of Arabic dialects, for which there is a lack of linguistic studies, notably on prosodic comparisons. We show that our system is able to clearly identify the dialectal areas, leading to the hypothesis that those dialects have prosodic differences.