Graphical analysis of hidden Markov model experiments
نویسنده
چکیده
Hidden Markov models are powerful tools for acoustic modeling in speech recognition systems. However, detailed analysis of their performance in specific experiments can be difficult. Two tools were developed and implemented for the purpose of analyzing hidden Markov model experiments: an interactive Viterbi backtrace viewer, and a multi-dimensional scaling display. These tools were built using the HMM Toolkit (HTK). Use of the Viterbi backtrace tool provided insight that eventually led to improved recognition performance. Thesis Supervisor: Victor W. Zue Title: Principal Research Scientist Associate Director, Laboratory of Computer Science Thesis Supervisor: Marc A. Zissman Title: Staff Member, MIT Lincoln Laboratory
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