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» Signal Processing for Robust Speech Recognition
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TSD
2007
Springer
16 years 7 days ago
Maximum Likelihood and Maximum Mutual Information Training in Gender and Age Recognition System
Abstract. Gender and age estimation based on Gaussian Mixture Models (GMM) is introduced. Telephone recordings from the Czech SpeechDatEast database are used as training and test d...
Valiantsina Hubeika, Igor Szöke, Lukas Burget...
ICASSP
2010
IEEE
15 years 6 months ago
Maximum-likelihood-based cepstral inverse filtering for blind speech dereverberation
Current state-of-the-art speech recognition systems work quite well in controlled environments but their performance degrades severely in realistic acoustical conditions in reverb...
Kshitiz Kumar, Richard M. Stern
SPEECH
2008
97views more  SPEECH 2008»
15 years 6 months ago
A new approach for the adaptation of HMMs to reverberation and background noise
Looking at practical application scenarios of speech recognition systems several distortion effects exist that have a major influence on the speech signal and can considerably det...
Hans-Günter Hirsch, Harald Finster
NIPS
2003
15 years 7 months ago
A Mixed-Signal VLSI for Real-Time Generation of Edge-Based Image Vectors
A mixed-signal image filtering VLSI has been developed aiming at real-time generation of edge-based image vectors for robust image recognition. A four-stage asynchronous median de...
Masakazu Yagi, Hideo Yamasaki, Tadashi Shibata
INTERSPEECH
2010
15 years 29 days 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...