An Experimental Comparison of Modeling Techniques and Combination of Speaker – Specific Information from Different Languages for Multilingual Speaker Identification
| Autor: | H. S. Jayanna, B. G. Nagaraja |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Computer science
Science Speech recognition computer.software_genre 030507 speech-language pathology & audiology 03 medical and health sciences Artificial Intelligence speaker identification business.industry Specific-information QA75.5-76.95 Speaker recognition modeling techniques Speaker diarisation Electronic computers. Computer science Speaker identification Artificial intelligence multilingual 0305 other medical science business computer speaker-specific information Software Natural language processing Information Systems |
| Zdroj: | Journal of Intelligent Systems, Vol 25, Iss 4, Pp 529-538 (2016) |
| ISSN: | 2191-026X 0334-1860 |
| Popis: | Most of the state-of-the-art speaker identification systems work on a monolingual (preferably English) scenario. Therefore, English-language autocratic countries can use the system efficiently for speaker recognition. However, there are many countries, including India, that are multilingual in nature. People in such countries have habituated to speak multiple languages. The existing speaker identification system may yield poor performance if a speaker’s train and test data are in different languages. Thus, developing a robust multilingual speaker identification system is an issue in many countries. In this work, an experimental evaluation of the modeling techniques, including self-organizing map (SOM), learning vector quantization (LVQ), and Gaussian mixture model-universal background model (GMM-UBM) classifiers for multilingual speaker identification, is presented. The monolingual and crosslingual speaker identification studies are conducted using 50 speakers of our own database. It is observed from the experimental results that the GMM-UBM classifier gives better identification performance than the SOM and LVQ classifiers. Furthermore, we propose a combination of speaker-specific information from different languages for crosslingual speaker identification, and it is observed that the combination feature gives better performance in all the crosslingual speaker identification experiments. |
| Databáze: | OpenAIRE |
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