Embedded Files
Language Recognition from Speech Signals
Language Recognition from Speech Signals
Language recognition is the task of recognizing the spoken language in a speech utterance. It has applications in multilingual translation systems, automatic speech recognition, targeted advertising, forensics and biometric authentication.
In this project, we aimed to develop a robust language recognition system.
The 2015 NIST Language Recognition Evaluation: The Shared View of I2R, Fantastic4 and SingaMS
The 2015 NIST Language Recognition Evaluation: The Shared View of I2R, Fantastic4 and SingaMS
In this paper, we presented a shared view of five institutions resulting from our collaboration towards Language Recognition Evaluation 2015 submissions. We have witnessed a major paradigm shift in adopting deep neural network (DNN) for both feature extraction and classifier.
K. A. Lee, H. Li, L. Deng, V. Hautamäki, W. Rao, X. Xiao, A. Larcher, H. Sun, T. Nguyen, G. Wang, A. Sizov, J. Chen, I. Kukanov, A. H. Poorjam, T. Trong, C. L. Xu, H. H. Xu, B. Ma, E. S. Chng, S. Meignier
in Proc. INTERSPEECH 2016, San Francisco, US, September 2016.
[Paper] & [Technical Report]
A0_size_amir_odyssey2016_poster.pdfIncorporating Uncertainty as a Quality Measure in I-Vector Based Language Recognition
Incorporating Uncertainty as a Quality Measure in I-Vector Based Language Recognition
In this work, we introduced a new quality measure which represents the uncertainty in estimation of the i-vectors extracted from speech signals. Then, we incorporate it into the recognition process to improve the language recognition accuracy.
Amir H. Poorjam, Rahim Saeidi, Tomi Kinnunen, Ville Hautamäki
in Proc. Speaker and Language Recognition Workshop (Odyssey), pp. 74–80, Bilbao, Spain, June 2016. [PDF]
odyssey_Full HD.mp4Presented by Dr. Rahim Saeidi
Google Sites
Report abuse