Parallel algorithms for isolated and connected word recognition. Volumes I and II
For years researchers have worked toward finding a way to allow people to talk to machines in the same manner a person communicates to another person. This verbal man to machine interface, called speech recognition, can be grouped into three types: isolated word recognition, connected word recognition, and continuous speech recognition. Isolated word recognizers recognize single words with distinctive pauses before and after them. Continuous speech recognizers recognize speech spoken as one person speaks to another, continuously without pauses. Connected word recognition is an extension of isolated word recognition which recognizes groups of words spoken continuously. A group of words must have distinctive pauses before and after it, and the number of words in a group is limited to some small value (typically less than six). If these types of recognition systems are to be successful in the real world, they must be speaker independent and support a large vocabulary. They also must be able to recognize the speech input accurately and in real time. Currently there is no system which can meet all of these criteria because a vast amount of computations are needed. This thesis examines the use of parallel processing to reduce the computation time for speech recognition.
- Research Organization:
- Purdue Univ., Lafayette, IN (USA)
- OSTI ID:
- 5249564
- Resource Relation:
- Other Information: Thesis (Ph. D.)
- Country of Publication:
- United States
- Language:
- English
Similar Records
Speaker recognition through NLP and CWT modeling.
Speaker Recognition Through NLP and CWT Modeling