Coursera Flash Sale
40% Off Coursera Plus for 3 Months!
Grab it
Learn the fundamentals of speech recognition technology in this comprehensive lecture that explores the core principles, algorithms, and methodologies used to convert spoken language into text. Examine the acoustic modeling techniques that capture the relationship between audio signals and phonetic units, including hidden Markov models and their applications in speech processing. Discover how language models incorporate linguistic knowledge to improve recognition accuracy by predicting word sequences and handling ambiguities in spoken input. Explore the signal processing foundations that transform raw audio into feature representations suitable for machine learning algorithms. Understand the challenges of handling variability in speech due to different speakers, accents, speaking rates, and environmental conditions. Investigate the role of pronunciation modeling and how systems handle the gap between canonical pronunciations and actual speech production. Delve into search algorithms and decoding strategies that efficiently find the most likely word sequence given acoustic observations. Gain insights into evaluation metrics used to assess speech recognition system performance and the trade-offs between accuracy and computational efficiency in real-world applications.
