%0 Journal Article %1 Polur2005 %A Polur, Prasad D %A Miller, Gerald E %D 2005 %J IEEE Trans Neural Syst Rehabil Eng %K Algorithms; Art; Cerebral Palsy; Dysarthria; Fourier Analysis; Humans; Linear Models; Markov Chains; Models, Biological; Pattern Recognition, Automated; Signal Processing, Computer-Assisted; Speech Recognition Software; ificial Intelligence %N 4 %P 558--561 %T Experiments with fast Fourier transform, linear predictive and cepstral coefficients in dysarthric speech recognition algorithms using hidden Markov Model. %V 13 %X In this study, a hidden Markov Model was constructed and conditions were investigated that would provide improved performance for a dysarthric speech (isolated word) recognition system. The speaker dependant system was intended to act as an assistive/control tool. A small size vocabulary spoken by three cerebral palsy subjects was chosen. Fast Fourier transform, linear predictive, and Mel frequency cepstral coefficients extracted from data provided training input to several whole-word hidden Markov model configurations. The effect of model structure, number of states, and frame rates were also investigated. It was noted that a 10-state ergodic model using 15 msec frames was better than other configurations. Furthermore, it was found that a Mel cepstrum based model outperformed a fast Fourier transform and linear prediction based model. The system offers effective and robust application as a rehabilitation and/or control tool to assist dysarthric motor impaired individuals.

@article{Polur2005, abstract = {In this study, a hidden Markov Model was constructed and conditions were investigated that would provide improved performance for a dysarthric speech (isolated word) recognition system. The speaker dependant system was intended to act as an assistive/control tool. A small size vocabulary spoken by three cerebral palsy subjects was chosen. Fast Fourier transform, linear predictive, and Mel frequency cepstral coefficients extracted from data provided training input to several whole-word hidden Markov model configurations. The effect of model structure, number of states, and frame rates were also investigated. It was noted that a 10-state ergodic model using 15 msec frames was better than other configurations. Furthermore, it was found that a Mel cepstrum based model outperformed a fast Fourier transform and linear prediction based model. The system offers effective and robust application as a rehabilitation and/or control tool to assist dysarthric motor impaired individuals.}, added-at = {2014-07-19T21:03:42.000+0200}, author = {Polur, Prasad D and Miller, Gerald E}, biburl = {https://www.bibsonomy.org/bibtex/2b64d8c59401eed3a66421bada9246cd9/ar0berts}, groups = {public}, interhash = {93c13c4b8b25cfa12baf1a2bdc07687b}, intrahash = {b64d8c59401eed3a66421bada9246cd9}, journal = {IEEE Trans Neural Syst Rehabil Eng}, keywords = {Algorithms; Art; Cerebral Palsy; Dysarthria; Fourier Analysis; Humans; Linear Models; Markov Chains; Models, Biological; Pattern Recognition, Automated; Signal Processing, Computer-Assisted; Speech Recognition Software; ificial Intelligence}, month = Dec, number = 4, pages = {558--561}, pmid = {16425838}, timestamp = {2014-07-19T21:03:42.000+0200}, title = {Experiments with fast Fourier transform, linear predictive and cepstral coefficients in dysarthric speech recognition algorithms using hidden Markov Model.}, username = {ar0berts}, volume = 13, year = 2005 }