Noise-dependent Gaussian mixture classifiers for robust rejection decision.

Noise-dependent Gaussian mixture classifiers for robust rejection decision.

Speech or speaker recognizers need to make a decision on either accepting or rejecting a recognized item, based on some measurement (e.g., likelihood) associated to the item. Distribution-based classification can be used to make the decision. In practical applications, the background noise level may...

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Detalhes bibliográficos
Publicado no:IEEE Transactions on speech and audio processing 10, 2 (2002).
Autor principal: Yifan Gong
Formato: Artigo
Idioma:English
Assuntos:
EM algorithm.
Background noise level.
Decision mechanism.
Decision parameters.
Distribution-based classification.
False acceptance.
False rejection.
Highway driving conditions.
In-car speech data.
Multidimensional Gaussian distributions.
Noise level.
Noise-dependent Gaussian mixture classifiers.
Parked car.
Polynomial function.
Robust rejection decision.
Speaker recognizers.
Speech recognizers.