CONF Roy_ACMSAC2010_2010/IDIAP Visual processing-inspired Fern-Audio features for Noise-Robust Speaker Verification Roy, Anindya Marcel, Sébastien EXTERNAL https://publications.idiap.ch/attachments/papers/2009/Roy_ACMSAC2010_2010.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/Roy_Idiap-RR-29-2009 Related documents Association for Computing Machinery - ACM 25th Symposium on Applied Computing, 2010, Sierre, Switzerland 2010 March 2010 In this paper, we consider the problem of speaker verification as a two-class object detection problem in computer vision, where the object instances are 1-D short-time spectral vectors obtained from the speech signal. More precisely, we investigate the general problem of speaker verification in the presence of additive white Gaussian noise, which we consider as analogous to visual object detection under varying illumination conditions. Inspired by their recent success in illumination-robust object detection, we apply a certain class of binary-valued pixel-pair based features called Ferns for noise-robust speaker verification. Intensive experiments on a benchmark database according to a standard evaluation protocol have shown the advantage of the proposed features in the presence of moderate to extremely high amounts of additive noise. REPORT Roy_Idiap-RR-29-2009/IDIAP Visual processing-inspired Fern-Audio features for Noise-Robust Speaker Verification Roy, Anindya Marcel, Sébastien EXTERNAL https://publications.idiap.ch/attachments/reports/2009/Roy_Idiap-RR-29-2009.pdf PUBLIC Idiap-RR-29-2009 2009 Idiap November 2009 In this paper, we consider the problem of speaker verification as a two-class object detection problem in computer vision, but the object instances are 1-D short-time spectral vectors obtained from the speech signal. More precisely, we investigate the general problem of speaker verification in the presence of additive white Gaussian noise, which we consider as analogous to visual object detection under varying illumination conditions. Inspired by their recent success in illumination-robust object detection, we apply a certain class of binary-valued pixel-pair based features called Ferns for noise-robust speaker verification. Intensive experiments on a benchmark database according to a standard evaluation protocol have shown the advantage of the proposed features in the presence of moderate to extremely high amounts of additive noise.