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In this chapter we will present the progress made in automatic speech recognition for Romanian language based on the ASRS_RL (Automatic Speech Recognition System for Romanian Language) research platform. Speech recognition is a research domain with a long history, but despite this fact, still open for new investigations and answers to the not yet finally solved questions. This situation can be explained by the difficulty of the task, underlying on the fact that speech is a human product, with a high degree of correlation in content, but with a great variability in the formal manifestation as an acoustic signal. Great difficulties cause also the imperfection of the audio chain and the noises in the environment. The best-known strategies for speech recognition are the statistical and the connectionist ones, but fuzzy sets can also play an important role. Based on HMM’s the statistical strategies have many advantages, among them being recalled: rich mathematical framework, powerful learning and decoding methods, good sequences handling capabilities, flexible topology for statistical phonology and syntax. The disadvantages lie in the poor discrimination between the models and in the unrealistic assumptions that must be made to construct the HMM’s theory, namely the independence of the successive feature frames (input vectors) and the first order Markov process. Based on artificial neural networks (ANNs), the connectionist strategies for speech recognition have the advantages of the massive parallelism, good adaptation, efficient algorithms for solving classification problems and intrinsic discriminative properties. However, the neural nets have difficulties in handling the temporal dependencies inherent in speech data. The learning capabilities of the statistical and the neural models are very important, classifier built on such bases having the possibility to recognize new, unknown patterns with the experience obtained by training. The introduction of fuzzy sets allows on one hand the so-called fuzzy decisions, on other hand the “fuzzyfication” of input data, often more suitable for recognition of pattern produced by human beings, by speaking, for example. In a fuzzy decision, the recognizer realizes the classification based on the degree of membership to a given class for the pattern to be classified, a pattern belonging in a certain measure to each of the possible classes. This O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m

Progress in Speech Recognition for Romanian Language