Graphical Analysis of Hidden Markov Model Speech Recognition Experiments

摘要

Hidden Markov models are powerful tools for acoustic modeling III speechrecognition 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 multidimensional scaling display. These tools were built using the HMM Toolkit. Use of the Viterbi backtrace tool provided insight that eventually led to improved recognition performance. (AN).