Valsan, Zica; Gavat, Inge; Sabac, Bogdan; Cula, Oana; Grigore, Ovidiu; Militaru, Diana; Dumitru, Octavian Statistical and hybrid methods for speech recognition in Romanian. (English) Zbl 1008.68621 International Journal of Speech Technology 5, No. 3, 259-268 (2002). Summary: The present paper describes the evolution of our work concerning the problem of speech recognition. Beginning with a classical hidden Markov model (HMM), we have investigated two ways to improve the performance of this basic structure. The first way was to realize a neuro-statistical hybrid by integrating a multilayer perceptron (MLP) as a posteriori probability estimator. The system was further refined by adding supplementary discriminative training (DT) based on the minimum classification error (MCE). Tests performed on a 15,000 isolated spoken-word database, showed an increase in the recognition rate from 92.2% for the HMM-based recognition system, to 94.7% for the HMM-MLP system, and then to 98.1% for the refined HMM-MLP-DT system. The second way to improve the classical HMM was to build a fuzzy-statistical hybrid, FHMM, based on a fuzzy similarity measure instead of the probabilistic measure specific to the usual statistical model. The benefits of the fuzzy measure introduction were evaluated on a vowel recognition task, and a decrease of approximately 3% in the error rate is reported. MSC: 68U99 Computing methodologies and applications 68T50 Natural language processing Keywords:statistical models; hybrid recognizers; probabilistic similarity measure; fuzzy similarity measure PDFBibTeX XMLCite \textit{Z. Valsan} et al., Int. J. Speech Technol. 5, No. 3, 259--268 (2002; Zbl 1008.68621) Full Text: DOI