Maximum likelihood stochastic transformation adaptation for medium and small data sets

Maximum likelihood stochastic transformation adaptation for medium and small data sets

Speaker adaptation is recognized as an essential part of today’s large-vocabulary automatic speech recognition systems. A family of techniques that has been extensively applied for limited adaptation data is transformation-based adaptation. In transformation-based adaptation we partition our parameter space in a set of classes, estimate a transform (usually linear) for each class and apply the ...

Maximum-likelihood stochastic-transformation adaptation of hidden Markov models

The recognition accuracy in recent large vocabulary automatic speech recognition (ASR) systems is highly related to the existing mismatch between the training and testing sets. For example, dialect differences across the training and testing speakers result to a significant degradation in recognition performance. Some popular adaptation approaches improve the recognition performance of speech r...

Generalization and Maximum Likelihood from Small Data Sets

Efficient maximum likelihood classification for imaging spectrometer data sets

A simplified maximum likelihood classification technique for handling remotely sensed image data is proposed which reduces, significantly, the processing time associated with traditional maximum likelihood classification when applied to imaging spectrometer data, and copes with the training of geographically small classes. Several wavelength subgroups are formed from the complete set of spectra...

A fast maximum likelihood nonlinear feature transformation method for GMM-HMM speaker adaptation

We describe a novel maximum likelihood nonlinear feature bias compensation method for Gaussian mixture model–hidden Markov model (GMM–HMM) adaptation. Our approach exploits a single-hiddenlayer neural network (SHLNN) that, similar to the extreme learning machine (ELM), uses randomly generated lower-layer weights and linear output units. Different from the conventional ELM, however, our approach...