The tutorial account of hidden Markov models

Rabiner wrote an exhaustive guide: the three HMM problems, the algorithm for each, and the engineering detail of implementation.

Why it matters

A method whose theory sat in mathematical journals that engineers seldom read, and whose first applications gave too little tutorial detail, became accessible to any engineer, and that is what carried it out of the laboratories.

The paper works through evaluating likelihood, finding the best state sequence and training the parameters, along with the practical traps: scaling, multiple observations, initialisation. It is among the most cited texts in the history of signal processing. Its role is telling: a field is moved not only by a new idea but by whoever makes an existing one usable.

Event record

Event date

February 1989

Timeline date

Event date

Verification

Sources gathered automatically · September 17, 2026

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evt-0194

The February 1989 issue of the Proceedings of the IEEE.

Sources

primary A tutorial on hidden Markov models and selected applications in speech recognition
Proceedings of the IEEE · Published February 1989
primary A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition (Lawrence R. Rabiner), Proceedings of the IEEE 77(2), 257-286, February 1989
IEEE (Proceedings of the IEEE), copy hosted by Carnegie Mellon University · Published February 1989

Related events

Earlier

Related Sphinx 1988
It describes the apparatus the systems of the same period are built on.
Extends The Baum-Welch algorithm 1970
The paper turns Baum's mathematics into practical implementation recipes.
- A tutorial on hidden Markov models and selected applications in speech recognition · Proceedings of the IEEE

Later

Builds on Sign language sentences from video 1995links to this record
The abstract carries hidden Markov models over to gesture from speech recognition, where they had been used "prominently and successfully".
- Real-time American Sign Language recognition from video using hidden Markov models (T. Starner, A. Pentland), Proceedings of the International Symposium on Computer Vision, 265-270 · IEEE (ISCV 1995, Coral Gables)
Related Unit selection: a voice from pieces of recording 1996links to this record
The paper itself reduces the unit database to an ergodic HMM and cites Rabiner's tutorial; the difference is that its costs are not probabilities.
- Unit selection in a concatenative speech synthesis system using a large speech database (Andrew J. Hunt, Alan W. Black), Proc. ICASSP-96, vol. 1, pp. 373-376, Atlanta, 7-10 May 1996 · IEEE; author copy on Alan W. Black's publications page
Related A voice from a statistical model: HTS 1999links to this record
Here the hidden Markov model no longer recognises speech but produces it: spectrum, pitch and duration come out of the same model.
Builds on Conditional random fields 2001links to this record
CRFs are framed as an answer to a limit of hidden Markov models, which cannot represent many overlapping features of the observation; on the same Penn treebank data the paper compares CRFs with HMMs.
- Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data · University of Pennsylvania, ScholarlyCommons (Departmental Papers, CIS)
Related Deep networks in speech recognition 2009links to this record
The Gaussian mixtures standard since Rabiner's tutorial are replaced by a network.