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» On the use of speaker superfactors for speaker recognition
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ICASSP
2009
IEEE
14 years 2 months ago
Lattice-based MLLR for speaker recognition
Maximum-Likelihod Linear Regression (MLLR) transform coefficients have shown to be useful features for text-independent speaker recognition systems. These use MLLR coefficients ...
Marc Ferras, Claude Barras, Jean-Luc Gauvain
ISCAS
2008
IEEE
139views Hardware» more  ISCAS 2008»
14 years 1 months ago
Missing feature speech recognition in a meeting situation with maximum SNR beamforming
Abstract— Especially for tasks like automatic meeting transcription, it would be useful to automatically recognize speech also while multiple speakers are talking simultaneously....
Dorothea Kolossa, Shoko Araki, Marc Delcroix, Tomo...
ESORICS
2010
Springer
13 years 5 months ago
Speaker Recognition in Encrypted Voice Streams
Transmitting voice communication over untrusted networks puts personal information at risk. Although voice streams are typically encrypted to prevent unwanted eavesdropping, additi...
Michael Backes, Goran Doychev, Markus Dürmuth...
ICASSP
2010
IEEE
13 years 5 months ago
A comparison of approaches for modeling prosodic features in speaker recognition
Prosodic information has been successfully used for speaker recognition for more than a decade. The best-performing prosodic system to date has been one based on features extracte...
Luciana Ferrer, Nicolas Scheffer, Elizabeth Shribe...
INTERSPEECH
2010
13 years 2 months ago
What else is new than the hamming window? robust MFCCs for speaker recognition via multitapering
Usually the mel-frequency cepstral coefficients (MFCCs) are derived via Hamming windowed DFT spectrum. In this paper, we advocate to use a so-called multitaper method instead. Mul...
Tomi Kinnunen, Rahim Saeidi, Johan Sandberg, Maria...