Finite State Transducer

Finite-state transducers (FSTs) are mathematical models representing input-output mappings, crucial for tasks involving sequence transformations like speech recognition and natural language processing. Current research emphasizes efficient decoding algorithms, particularly for large-scale applications, and explores hybrid models combining FSTs with neural networks to leverage the strengths of both approaches, such as improved accuracy and handling of ambiguous inputs. This work is significant for advancing both theoretical understanding of computation and practical applications, leading to faster, more accurate, and more robust systems in areas like speech recognition, machine translation, and complex event processing.