Combination of Classifiers Decisions for Multilingual Speaker Identification

Abstract

State-of-the-art speaker recognition systems may work better for the English language. However, if the same system is used for recognizing those who speak different languages, the systems may yield a poor performance. In this work, the decisions of a Gaussian mixture model-universal background model (GMM-UBM) and a learning vector quantization (LVQ) are combined to improve the recognition performance of a multilingual speaker identification system. The difference between these classifiers is in their modeling techniques. The former one is based on probabilistic approach and the latter one is based on the fine-tuning of neurons. Since the approaches are different, each modeling technique identifies different sets of speakers for the same database set. Therefore, the decisions of the classifiers may be used to improve the performance. In this study, multitaper mel-frequency cepstral coefficients (MFCCs) are used as the features and the monolingual and cross-lingual speaker identification studies are conducted using NIST-2003 and our own database. The experimental results show that the combined system improves the performance by nearly 10% compared with that of the individual classifier.

Novel Approaches for Applying Linguistic Processing Techniques Based on Pattern Recognition and Machine Learning

“…One of their important contributions is that the weighted cluster coefficients are integrated so that their model can infer the weighted contribution at the prediction stage. Nagaraja and Jayanna [33] propose a new approach combining Gaussian mixture model-universal background model (GMM-UBM) and learning vector quantization (LVQ)-based classifiers for monolingual and cross-lingual speaker identification using multi-taper mel-frequency cepstral coefficient (MFCC) features. The results show that their proposed combination system can be used to improve the multilingual speaker identification process.…”