Method and system for training of a classifier

Details Method and system for training of a classifier Method and system for training of a classifier

2000-07-31

2004-04-27

Abebe, Daniel (Department: 2641)

Data processing: speech signal processing, linguistics, language

Speech signal processing

Recognition

C704S250000

Reexamination Certificate

active

06728674

ABSTRACT:

BACKGROUND OF THE INVENTION
Speech recognition is a classification task. In maximum likelihood classifiers, each classifier is trained by examples that belong to its class. For example, the classifier which recognizes the digit “1” is trained by multiple pronunciations of the digit “1”.
A commonly used classifier is a Hidden Markov Model (HMM). Each word is modeled by a different HMM which serves as an abstract “picture” of this word, with all its possible variations. The HMM consists of a sequence of “states”, each state is responsible for the description of a different part of the word. The use of HMM in speech recognition consists of two phases: the training phase and the recognition phase. In the training phase, repetitions of each word from the training data are used to construct the corresponding HMM. In the recognition phase, the word models may be used to identify unknown speech by checking the unknown speech against the existing models.
Some words sound similar to each other and can therefore be incorrectly recognized. Using digits as examples, “go” (5) and “rok” (6) in Japanese and “seven” and “eleven” in English sound sufficiently similar to cause an incorrect recognition.


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