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Learn the fundamental concepts and methodologies of speech recognition technology in this comprehensive lecture delivered by Sanjeev Khudanpur from Johns Hopkins University's Center for Language & Speech Processing. Explore the core principles underlying automatic speech recognition systems, including signal processing techniques, acoustic modeling, language modeling, and decoding algorithms. Discover how speech recognition systems convert spoken language into text through statistical and computational approaches. Examine the challenges faced in speech recognition such as speaker variability, background noise, and linguistic diversity. Gain insights into the mathematical foundations that drive modern speech recognition technology, including hidden Markov models, neural networks, and probabilistic frameworks. Understand the evolution of speech recognition from early template-matching approaches to contemporary deep learning methodologies. Analyze real-world applications of speech recognition in various domains including telecommunications, assistive technology, and human-computer interaction. This foundational overview provides essential knowledge for students and researchers interested in computational linguistics, signal processing, and artificial intelligence applications in speech technology.
