Statistical methods for speech recognition
Statistical methods for speech recognition
The Theory of Parsing, Translation, and Compiling
The Theory of Parsing, Translation, and Compiling
The EuTrans Spoken Language Translation System
Machine Translation
Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
Computational Complexity of Problems on Probabilistic Grammars and Transducers
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Finite-State Speech-to-Speech Translation
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 1 - Volume 1
The acoustic-modeling problem in automatic speech recognition
The acoustic-modeling problem in automatic speech recognition
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The stochastic extension of formal translations constitutes a suitable framework for dealing with many problems in Syntactic Pattern Recognition. Some estimation criteria have already been proposed and developed for the parameter estimation of Regular Syntax-Directed Translation Schemata. Here, a new criterium is proposed for dealing with situations when training data is sparse. This criterium is based on entropy measurements, somehow inspired in the Maximum Mutual Information criterium, and it takes into account the possibility of ambiguity in translations (i.e., the translation model may yield different output strings for a single input string.) The goal in the stochastic framework is to find the most probable translation of a given input string. Experiments were performed on a translation task which has a high degree of ambiguity.