1991 Fiscal Year Final Research Report Summary
Speech Recognition Based on Intelligent Beam Search Algorithm
| Project/Area Number |
01460254
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| Research Category |
Grant-in-Aid for General Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Informatics
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| Research Institution | Yamagata University |
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Principal Investigator |
KOHDA Masaki Yamagata University, Faculty of Engineering, Professor, 工学部, 教授 (00205337)
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| Project Period (FY) |
1989 – 1991
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| Keywords | Speech Recognition / Graph Search / A^* Algorithm / Dynamic Time Warping / Beam Search / Vector Quantization / Hidden Markov Model / Best-First Search |
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| Research Abstract |
In a large-vocabulary continuous speech recognition, an investigation of efficient recognition algorithms is extremely important because of executing an enormous computation needed in a matching process within a realistic CPU time. Conventional recognition algorithms based on a dynamic time warping (DTW), a hidden Markov model (HMM) and so on are constructed on the base of an exhaustive search of possible combinations. A dynamic programming technique is introduced to execute the exhaustive search efficiently.
The matching process in DTW-based and HMM-based speech recognition systems is regarded as a problem of searching an optimal path through a constrained node. In an application of graph searching algorithms to speechrecognition, two kinds of searching algorithms are effective, that is, a beam searching algorithm and a best-first searching algorithm. A conventional pruning strategy in speech recognition using the beam searching algorithm is based on only a score from the beginning node to the current node. A score estimate from the current node to the terminal node is not used. An A^* algorithm is introduced to speech recognition using the best-first searching algorithm.
This report describes new approaches to DTW-based and HMM-based speech recognition algorithms by modeling the matching process from a view point of a graph search. In Chapter I, a DTW-based speech recognition utilizing the beam searching algorithm is described. In Chapter II, a DTW-based speech recognition utilizing the best-first searching algorithm is described. Finally in Chapter III, an HMM-based speech recognition utilizing the best-first searching algorithm is described.
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