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Text-Dependent Speaker Identification Based on Input/Output HMMs:
An Empirical Study
ABSTRACT
In this paper, we explore the Input/Output HMM (IOHMM) architecture
for a substantial problem, that of text-dependent speaker identification.
For subnetworks modeled with generalized linear models, we extend the IRLS
algorithm to the M-step of the corresponding EM algorithm. Experimental
results show that the improved EM algorithm yields significantly faster
training than the original one. In comparison with the multilayer perceptron,
the dynamic programming technique and hidden Markov models, we empirically
demonstrate that the IOHMM architecture is a promising way to text-dependent
speaker identification.
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