| Project/Area Number |
01460254
|
|---|
| Research Category |
Grant-in-Aid for General Scientific Research (B)
|
| Allocation Type | Single-year Grants |
|---|
| Research Field |
Informatics
|
|---|
| Research Institution | Yamagata University |
|---|
Principal Investigator |
KOHDA Masaki Yamagata University, Faculty of Engineering, Professor, 工学部, 教授 (00205337)
|
|---|
| Project Period (FY) |
1989 – 1991
|
| Project Status |
Completed (Fiscal Year 1991)
|
| Budget Amount *help |
¥6,900,000 (Direct Cost: ¥6,900,000)
Fiscal Year 1991: ¥200,000 (Direct Cost: ¥200,000)
Fiscal Year 1990: ¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 1989: ¥5,200,000 (Direct Cost: ¥5,200,000)
|
|---|
| Keywords | Speech Recognition / Graph Search / A^* Algorithm / Dynamic Time Warping / Beam Search / Vector Quantization / Hidden Markov Model / Best-First Search / DPマッチング / 予備選択 / DPビ-ムサ-チ / 閾値関数 / 枝刈 / フレ-ム同期DPマッチング |
|---|
| 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.
|
