Phonotactic language recognition using dynamic pronunciation and language branch discriminative information

• The theory of language branch in linguistics is introduced to phonotactic language recognition.
• Phoneme variability factor containing dynamic pronunciation information is investigated in phonotactic language recognition.
• The proposed PLBV method containes dynamic pronunciation and language branch discriminative information.
• The proposed method uses pronunciation phonotactic characteristics while it doesn’t involve fallible phoneme sequences.
• The proposed method applies well-performed factor analysis to phoneme-dependent features rich in information.

This paper presents our study of phonotactic language recognition system using dynamic pronunciation and language branch discriminative information. The theory of language branch in linguistics is introduced to language recognition, and phonotactic language branch variability (PLBV) method based on factor analysis is proposed. In our work, phoneme variability factor containing dynamic pronunciation information is investigated firstly. By concatenating low-dimensional phoneme variability factors in the language branch spaces, phonotactic language branch variability factor is obtained. Language models are trained within and between language branches with support vector machine (SVM). The proposed method uses dynamic and discriminative pronunciation phonotactic characteristics while it doesn’t involve fallible phoneme sequences. Results on 2011 NIST Language Recognition Evaluation (LRE) 30 s data set show that the proposed method outperforms parallel phoneme recognizer followed by vector space models (PPRVSM) and ivector systems, and obtains relative improvement of 28.2–72.0% in EER, minDCF and language-pair performance metrics significantly.