Title :
Stereo-based stochastic noise compensation based on trajectory GMMS
Author :
Zen, Heiga ; Nankaku, Yoshihiko ; Tokuda, Keiichi
Author_Institution :
Dept. of Comput. Sci., Nagoya Inst. of Technol., Nagoya
Abstract :
This paper proposes a novel stereo-based stochastic noise compensation technique based on trajectory GMMs. Although the GMM-based noise compensation techniques such as SPLICE work effective, their performance sometimes degrades due to the inappropriate dynamic characteristics caused by the frame-by-frame mapping. While the use of dynamic feature constraints on the mapping stage can alleviate this problem, it also introduces an inconsistency between training and mapping. The recently proposed trajectory GMM-based feature mapping technique can solve this inconsistency while keeping the benefits of the use of dynamic features, and offers an entire sequence-level transformation rather than the frame-by-frame mapping. Results from a noise compensation experiment on the AURORA-2 task show that the proposed trajectory GMM-based noise compensation technique outperforms the conventional ones.
Keywords :
Gaussian noise; speech recognition; Gaussian mixture models; feature mapping technique; speech recognition; stereo-based stochastic noise compensation; trajectory GMMS; Computer science; Degradation; Gaussian noise; Hidden Markov models; Mel frequency cepstral coefficient; Probability density function; Speech recognition; Statistics; Stochastic resonance; Trajectory; GMM-based mapping; dynamic features; noise compensation; speech recognition; trajectory GMM;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-2353-8
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2009.4960649