Lalit R. Bahl

In this course

Lalit R. Bahl was a foundational figure in statistical approaches to speech recognition, particularly through his work on maximum likelihood decoding methods that treated recognition as a probabilistic inference problem. His papers with Jelinek and Mercer established key techniques for modeling acoustic and linguistic uncertainty—including methods for handling channel noise and symbol errors—that became central to how machine learning systems learned to parse and understand human speech. In the course readings, Bahl's work appears as a crucial technical precedent for understanding how statistical models could be trained on data rather than hand-engineered, a principle that bridges early cybernetic approaches to communication with modern deep learning paradigms.

Mentioned in 1 reading