Investigating fuzzy methods for multilinguals peaker identification
speaker identification, speaker
recognition, signal processing, multilingual speech systems, Portuguese,
English, Mandarin.
Speech is a crucial ability for humans to interact and communicate.
Speech-based technologies are becoming more popular with speech interfaces,
real-time translation, and budget healthcare diagnosis. Thus, this work aims
to explore an important but under-investigated topic on the field: multilingual
speech recognition. We employed three languages: English, Brazilian
Portuguese, and Mandarin. To the best of our knowledge, those three languages
were not compared yet. The objectives are to explore Brazilian Portuguese in
comparison with the other two more well-investigated languages, by verifying
speaker recognition robustness in multilingual environments, and further
investigate fuzzy methods. We have performed an analysis for text-independent
speaker identification on closed-set using log-Energy, 13-MFCCs, Deltas, and
Double Deltas with four classifiers. The closed-set text-independent speaker
identification results indicated that this problem presents some robustness on
multilingual environments, since adding a second language, it degrades the
accuracy by 5.45\%, and 5.32\% for a three language dataset using an SVM
classifier.