A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition

VI Implementation of speech recognizers using HMMs

1. How does a discrete-HMM isolated word recognizer work?
   Compute spectral (e.g. LPC) feature vectors, map each to the nearest codebook vector by vector quantization, train one left-right HMM per word, and recognize by choosing the word whose model gives the highest likelihood (forward or Viterbi score).
   • Rabiner, 1989, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition (Proceedings of the IEEE), VI Implementation of speech recognizers using HMMs
2. What is segmental k-means training?
   Iteratively segment training sequences into states with the Viterbi algorithm under the current model, re-estimate each state’s parameters from the observations assigned to it (clustering them, for mixtures), and repeat until convergence; it gives good initial estimates for Baum–Welch.
   • Rabiner, 1989, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition (Proceedings of the IEEE), VI Implementation of speech recognizers using HMMs
3. How did Rabiner recognize connected digit strings with word HMMs?
   By concatenating single-word HMMs and using level building, a dynamic-programming search that finds the best sequence of word models (one per “level”) to match the whole utterance, without segmenting it first.
   • Rabiner, 1989, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition (Proceedings of the IEEE), VI Implementation of speech recognizers using HMMs