Research on the model of speech recognition and understanding by using hierarchical information feedback
| Autor: | Lin Biqin, Yuan Baozong, Jiang Minghu |
|---|---|
| Rok vydání: | 1999 |
| Předmět: |
Audio mining
Voice activity detection Artificial neural network business.industry Computer science Speech recognition Acoustic model Content-addressable memory computer.software_genre Speaker recognition Speech processing Speech analytics Artificial intelligence Electrical and Electronic Engineering business computer Natural language processing |
| Zdroj: | Journal of Electronics (China). 16:208-214 |
| ISSN: | 1993-0615 0217-9822 |
| DOI: | 10.1007/s11767-999-0017-3 |
| Popis: | In this paper according to the process of cognitive of human being to speech is put forward a model of speech recognition and understanding in a noisy environment. For speech recognition, two level modular Extended Associative Memory Neural Networks (EAMNN) are adopted. The learning speed is 9 times faster than that of the conventional BP net. It has high self-adaptability, robustness, fault toleration and associative memory ability to the noisy signals. To speech understanding, the structure of hierarchical analysis and examining faults which is a combination of statistic inference and syntactic rules is adopted, to pick up the candidates of the speech recognition and to predict the next word by the statistic inference base; and the syntactic rule base reduces effectively the recognition errors and candidates of acoustic level; then by comparing and rectifying errors through information feedback and guiding the succeeding speech process, the recognition of the sentence is realized. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink