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

I Introduction

1. What are the two broad classes of signal models Rabiner distinguishes?
   Deterministic models, which exploit known properties of the signal (a sine wave, a sum of exponentials) and only need parameter values; and statistical models (Gaussian, Poisson, Markov, hidden Markov processes), which characterize only the statistical properties, assuming the signal is a parametric random process.
   • Rabiner, 1989, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition (Proceedings of the IEEE), I Introduction
2. Why does a signal model matter, according to Rabiner?
   It is a basis for designing processing systems (e.g. removing noise); it lets us learn about and simulate the source without having it available; and, most importantly, such models often work extremely well in practice for prediction, recognition and identification.
   • Rabiner, 1989, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition (Proceedings of the IEEE), I Introduction
3. Why did HMMs take so long to spread in speech processing, though the theory dates from the late 1960s?
   Baum’s theory appeared in mathematical journals engineers didn’t read, and the early speech applications (Baker at CMU, Jelinek’s group at IBM) lacked tutorial material; tutorials like this one filled the gap.
   • Rabiner, 1989, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition (Proceedings of the IEEE), I Introduction