Method for improving results in an HMM-based segmentation...

Details Method for improving results in an HMM-based segmentation... Method for improving results in an HMM-based segmentation...

2005-11-15

2005-11-15

{hacek over (S)}mits, Talivaldis Ivars (Department: 2655)

Data processing: speech signal processing, linguistics, language

Speech signal processing

Recognition

C704S001000, C704S009000, C715S252000, C715S252000, C715S252000

Reexamination Certificate

active

06965861

ABSTRACT:
A Hidden Markov model is used to segment a data sequence. To reduce the potential for error that may result from the Markov assumption, the Viterbi dynamic programming algorithm is modified to apply a multiplicative factor if a particular set of states is re-entered. As a result, structural domain knowledge is incorporated into the algorithm by expanding the state space in the dynamic programming recurrence. In a specific example of segmenting resumes, the factor is used to reward or penalize (even require or prohibit) a segmentation of the resume that results in the re-entry into a section such as Experience or Contact Information. The method may be used to impose global constraints in the processing of an input sequence or to impose constraints to local sub-sequences.

REFERENCES:
patent: 6542635 (2003-04-01), Hu et al.
patent: 2002/0165717 (2002-11-01), Solmer et al.

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