%0 Conference Paper %1 salaja-evaluation-wains-speech-2015 %A Salaja, Rosemary T. %A Flynn, Ronan %A Russell, Michael %B Signals and Systems Conference (ISSC), 2015 26th Irish %D 2015 %K alife creatur speech_recognition wains %P 1--6 %R 10.1109/ISSC.2015.7163770 %T Evaluation of wains as a classifier for automatic speech recognition %U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7163770 %X This paper introduces a new back-end classifier for a speech recognition system that is based on artificial life (ALife). The ALife species being used for classification purposes are called wains, which were developed using the Créatúr framework. The speech recognition task used in the evaluation of the new classifier is that of isolated digit recognition. Performance of the proposed back-end classifier is benchmarked for comparison purposes against a hidden Markov model (HMM)-based classifier. Mel frequency cepstral coefficients extracted from the speech by a front-end processor are used by the two back-ends for classification of the speech utterances. In tests carried out to date, the performance of the proposed classifier falls short of the performance of the HMM-based classifier. However, the results suggest that with further training the recognition accuracy of the proposed classifier can be significantly improved.

@inproceedings{salaja-evaluation-wains-speech-2015, abstract = {This paper introduces a new back-end classifier for a speech recognition system that is based on artificial life (ALife). The ALife species being used for classification purposes are called wains, which were developed using the Créatúr framework. The speech recognition task used in the evaluation of the new classifier is that of isolated digit recognition. Performance of the proposed back-end classifier is benchmarked for comparison purposes against a hidden Markov model (HMM)-based classifier. Mel frequency cepstral coefficients extracted from the speech by a front-end processor are used by the two back-ends for classification of the speech utterances. In tests carried out to date, the performance of the proposed classifier falls short of the performance of the HMM-based classifier. However, the results suggest that with further training the recognition accuracy of the proposed classifier can be significantly improved.}, added-at = {2015-10-16T12:45:47.000+0200}, author = {Salaja, Rosemary T. and Flynn, Ronan and Russell, Michael}, biburl = {https://www.bibsonomy.org/bibtex/26ebdda2e0f4f5f4d19c58ff072f414da/mhwombat}, booktitle = {Signals and Systems Conference (ISSC), 2015 26th Irish}, doi = {10.1109/ISSC.2015.7163770}, interhash = {a68a58da6eeab6c4c3dddcaf07fc355e}, intrahash = {6ebdda2e0f4f5f4d19c58ff072f414da}, keywords = {alife creatur speech_recognition wains}, month = jun, pages = {1--6}, timestamp = {2016-07-12T19:25:30.000+0200}, title = {Evaluation of wains as a classifier for automatic speech recognition}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7163770}, year = 2015 }