Fundamentals of speech recognition
Fundamentals of speech recognition
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Pattern classification: a unified view of statistical and neural approaches
Pattern classification: a unified view of statistical and neural approaches
New approximations of differential entropy for independent component analysis and projection pursuit
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A Discriminative Segmental Speech Model and Its Application to Hungarian Number Recognition
TDS '00 Proceedings of the Third International Workshop on Text, Speech and Dialogue
The Modulation Spectrogram: In Pursuit of an Invariant Representation of Speech
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
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This paper examines the applicability of some learning techniques to the classification of phonemes. The methods tested were artificial neural nets (ANN), support vector machines (SVM) and Gaussian mixture modeling. We compare these methods with a traditional hidden Markov phoneme model (HMM) working with the linear prediction-based cepstral coefficient features (LPCC). We also tried to combine the learners with feature transformation methods, like linear discriminant analysis (LDA), principal component analysis (PCA) and independent component analysis (ICA). We found that the discriminative learners can attain the efficiency of the HMM, and after LDA they can attain practically the same score on only 27 features. PCA and ICA proved ineffective, apparently because of the discrete cosine transform inherent in LPCC.