%0 Journal Article %1 noauthororeditor %A Kumar, Kuldeep %D 2011 %J International Journal of Computer Science, Engineering and Applications (IJCSEA) %K Artificial Mel Network Speech Transfer cepstral coefficients frequency function. layer networks neural recognition %N 3 %P 1-10 %T AN ANALYSIS OF SPEECH RECOGNITION PERFORMANCE BASED UPON NETWORK LAYERS AND TRANSFER FUNCTIONS %U http://airccse.org/journal/ijcsea/papers/0611csea02.pdf %V 1 %X Speech is the most natural way of information exchange. It provides an efficient means of means of manmachine communication using speech interfacing. Speech interfacing involves speech synthesis and speech recognition. Speech recognition allows a computer to identify the words that a person speaks to a microphone or telephone. The two main components, normally used in speech recognition, are signal processing component at front-end and pattern matching component at back-end. In this paper, a setup that uses Mel frequency cepstral coefficients at front-end and artificial neural networks at back-end has been developed to perform the experiments for analyzing the speech recognition performance. Various experiments have been performed by varying the number of layers and type of network transfer function, which helps in deciding the network architecture to be used for acoustic modelling at back end.

@article{noauthororeditor, abstract = {Speech is the most natural way of information exchange. It provides an efficient means of means of manmachine communication using speech interfacing. Speech interfacing involves speech synthesis and speech recognition. Speech recognition allows a computer to identify the words that a person speaks to a microphone or telephone. The two main components, normally used in speech recognition, are signal processing component at front-end and pattern matching component at back-end. In this paper, a setup that uses Mel frequency cepstral coefficients at front-end and artificial neural networks at back-end has been developed to perform the experiments for analyzing the speech recognition performance. Various experiments have been performed by varying the number of layers and type of network transfer function, which helps in deciding the network architecture to be used for acoustic modelling at back end. }, added-at = {2018-02-14T06:15:59.000+0100}, author = {Kumar, Kuldeep}, biburl = {https://www.bibsonomy.org/bibtex/28b543166ea3fc74f0889e98edec32d49/ijcsea}, interhash = {a319cf97d8d4d8842eb506805108b0a5}, intrahash = {8b543166ea3fc74f0889e98edec32d49}, journal = {International Journal of Computer Science, Engineering and Applications (IJCSEA) }, keywords = {Artificial Mel Network Speech Transfer cepstral coefficients frequency function. layer networks neural recognition}, month = {June}, number = 3, pages = {1-10}, timestamp = {2018-02-14T06:15:59.000+0100}, title = {AN ANALYSIS OF SPEECH RECOGNITION PERFORMANCE BASED UPON NETWORK LAYERS AND TRANSFER FUNCTIONS}, url = {http://airccse.org/journal/ijcsea/papers/0611csea02.pdf}, volume = 1, year = 2011 }