Conference paper
Exploiting diversity for spoken term detection
Lidia Mangu, Hagen Soltau, et al.
ICASSP 2013
This paper exploits the fact that when GMM and SVM classifiers with roughly the same level of performance exhibit uncorrelated errors they can be combined to produce a better classifier. The gain accrues from combining the descriptive strength of GMM models with the discriminative power of SVM classifiers. This idea, first exploited in the context of speaker recognition [1, 2], is applied to speech recognition - specifically to a digit recognition task in a noisy environment - with significant gains in performance.
Lidia Mangu, Hagen Soltau, et al.
ICASSP 2013
Tara N. Sainath, Brian Kingsbury, et al.
ASRU 2013
Mukund Padmanabhan, George Saon, et al.
IEEE Transactions on Speech and Audio Processing
E. Jan, Jaime Botella Ordinas, et al.
ICSLP 2000