Method and apparatus for word speech recognition by pattern matching

1998 ◽  
Vol 104 (5) ◽  
pp. 2558
Author(s):  
Yoshio Nakadai
Keyword(s):  
Speech Recognition ◽  
Pattern Matching

scholarly journals Modeling of Speech Recognition Using Artificial Neural Network

2019 ◽  
Vol 8 (9) ◽  
pp. 1225-1229
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Pattern Matching ◽  
Back Propagation ◽  
Experimental Result ◽  
Communication Link ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
The Voice

Automatic speech recognition has attained a lot of significance as it can act as easy communication link between machines and humans. This mode of communication is easy for man to use as it is effortless and easy. Many approaches for extraction of the features of the speech and classification of speech have been considered. This paper unveils the importance of neutral network and the way it can be used for recognition of speech. Mel Frequency Cepstrum Coefficients is made use of for extraction of the features from the voice. For pattern matching neural network has been used. MATLAB has been used to show how the speech is recognized. In this paper the speech recognition has been done firstly by multilayer feed forward neural network using Back propagation algorithm. Then the process of speech recognition is shown by using Radial basis function neural network. The paper then analyzes the performance of both the algorithms and experimental result shows that BPNN outperforms the RBFNN.

scholarly journals Speech Recognition by Formant Pattern Matching in N‐dimensional Space

1964 ◽  
Vol 36 (5) ◽  
pp. 1031-1031
Author(s):  
S. J. Campanella ◽  
D. C. Coulter ◽  
P. Engler
Keyword(s):  
Speech Recognition ◽  
Pattern Matching ◽  
Dimensional Space

Speech recognition processors using fuzzy pattern matching

2003 ◽  
Author(s):  
T. Ariyoshi ◽  
S. Kuriki ◽  
T. Kawamoto ◽  
S. Yasuda
Keyword(s):  
Speech Recognition ◽  
Pattern Matching ◽  
Fuzzy Pattern

The Design of Isolated Word Recognition Based on ARM9

2010 ◽  
Vol 171-172 ◽  
pp. 104-108
Author(s):  
Hui Pan ◽  
Wen Shang Xu ◽  
Xiu Jing Wang
Keyword(s):  
Speech Recognition ◽  
Word Recognition ◽  
Pattern Matching ◽  
Characteristic Parameters ◽  
Actual Usage ◽  
Great Progress ◽  
Isolated Word ◽  
Isolated Word Recognition

Nowadays,the theory and technology of speech recognition have made great progress,but in actual usage,they worked not very well.For some usage on some certain conditions ,we designed a small list, isolated word recognizer. With this system, we chosed ARM9 as the processor,MFCC as the characteristic parameters of speech sequences and adopted DTW to do pattern matching, As a result,it achieved hign rate of recognition and performed perfectly.

Selecting the Optimal FM System for Children With Cochlear Implants

2008 ◽  
Vol 18 (1) ◽  
pp. 19-24
Author(s):  
Erin C. Schafer
Keyword(s):  
Speech Recognition ◽  
Cochlear Implants ◽  
Empirical Research ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Evidence Based ◽  
Signal To Noise ◽  
Speech Processor ◽  
System Input ◽  
Optimal Type

Children who use cochlear implants experience significant difficulty hearing speech in the presence of background noise, such as in the classroom. To address these difficulties, audiologists often recommend frequency-modulated (FM) systems for children with cochlear implants. The purpose of this article is to examine current empirical research in the area of FM systems and cochlear implants. Discussion topics will include selecting the optimal type of FM receiver, benefits of binaural FM-system input, importance of DAI receiver-gain settings, and effects of speech-processor programming on speech recognition. FM systems significantly improve the signal-to-noise ratio at the child's ear through the use of three types of FM receivers: mounted speakers, desktop speakers, or direct-audio input (DAI). This discussion will aid audiologists in making evidence-based recommendations for children using cochlear implants and FM systems.

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