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Automatic Speech Recognition(ASR) systems are limited in the computational power and memory resources, especially in low-memory/low-power environments such as personal digital assistants. The parameter quantization is the one of the ways to achieve these conditions. In this work, we compare various subvector clustering procedures for the parameter(More)
This paper focuses on detection of a single emotion and verification of a specific emotion type in a test utterance. To utilize a probabilistic output of a classifier as well as to exploit various long term acoustic features, we built a prob-abilistic output SVM and applied several approximated log likelihood ratio tests for emotion verification.(More)
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