A comparative study of feature extraction methods applied to continuous speech recognition in Romanian language

This paper describes continuous speech recognition experiments on a Romanian language speech database, by using Hidden Markov Models (HMM). We compare the recognition rates obtained in our ASR system realising front-ends based on features extracted by perceptual variants of cepstral analysis and linear prediction and by simple linear prediction. The best results obtained with 36 coefficients mel-frecquency cepstral coefficients (MFCC) are used as basis to rank the front-ends based on LPC. The second rank is very promising for the performance obtained with 5 perceptual linear prediction (PLP) coefficients, obviously better at the last ranked performance of the simple linear prediction coefficients (LPC). We reorganized the database as follows: one database for male speakers, one database for female speakers and one database for both male and female speakers. http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4127501