Abstract
Although initially introduced and studied in the late 1960s and
early 1970s, statistical methods of Markov source or hidden Markov
modeling have become increasingly popular in the last several
years. There are two strong reasons why this has occurred. First the
models are very rich in mathematical structure and hence can form
the theoretical basis for use in a wide range of applications. Sec-
ond the models, when applied properly, work very well in practice
for several important applications. In this paper we attempt to care-
fully and methodically review the theoretical aspects of this type
of statistical modeling and show how they have been applied to
selected problems in machine recognition of speech.
- 2000
- analysis,
- applications
- bachelor:2015:elger
- book
- chain
- chains,
- citedby:scholar:count:24774
- citedby:scholar:timestamp:2017-4-5
- ctii:ws1213
- discrete
- diss
- dm-alg
- ergodic
- final
- function,
- hidden
- hidden.markov.model
- hmm
- imported
- inthesis
- jabref:nokeywordassigned
- markov
- markov-models
- master
- ml
- model
- modelling,
- models,
- nlp
- pattern
- probabilistic
- process
- processes,
- processing,
- recognition
- recognition,
- signal
- speech
- speech-recognition
- speech_recognition
- states,
- statistical
- stochastic
- temperature,
- thema:hmm
- tutorial
- uni
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