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 (EMM). 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-frequency 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