Learn · Brains, Minds and Machines Summer Course
Lecture 36 of 52Unit 7. Audition and Speech1 h 4 min
Lecture 7.4: Hynek Hermansky - Auditory Perception in Speech Technology Part 1
Prof. Tomaso Poggio and Prof. Gabriel Kreiman · Massachusetts Institute of Technology
Credits and licence
- From
- Brains, Minds and Machines Summer Course, Massachusetts Institute of Technology (at MIT OpenCourseWare)
- Licence
- CC BY-NC-SA 4.0, as MIT OpenCourseWare states it. Non-commercial use only.
- Attribution
- Prof. Tomaso Poggio, Prof. Gabriel Kreiman. Brains, Minds and Machines Summer Course. Summer 2015. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu/. License: Creative Commons BY-NC-SA.
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- Teens profiles and older. How courses are placed
All the lectures
- Lecture 0: Tomaso Poggio - Introduction to Brains, Minds, & Machines
- Lecture 1.1: Nancy Kanwisher - Human Cognitive Neuroscience
- Lecture 1.2: Gabriel Kreiman - Computational Roles of Neural Feedback
- Lecture 1.3: James DiCarlo - Neural Mechanisms of Recognition Part 1
- Lecture 1.4: James DiCarlo - Neural Mechanisms of Recognition Part 2
- Lecture 1.5: Winrich Freiwald - Primates, Faces, & Intelligence
- Lecture 1.6: Matt Wilson - Hippocampus, Memory, & Sleep Part 1
- Lecture 1.7: Matt Wilson - Hippocampus, Memory, & Sleep Part 2
- Seminar 1: Larry Abbott - Mind in the Fly Brain
- Lecture 2.1: Josh Tenenbaum - Computational Cognitive Science Part 1
- Lecture 2.2: Josh Tenenbaum - Computational Cognitive Science Part 2
- Lecture 2.3: Josh Tenenbaum - Computational Cognitive Science Part 3
- Lecture 3.1: Liz Spelke - Cognition in Infancy Part 1
- Lecture 3.2: Liz Spelke - Cognition in Infancy Part 2
- Lecture 3.3: Alia Martin - Developing an Understanding of Communication
- Lecture 3.4: Laura Schulz - Childrens' Sensitivity to Cost and Value of Information
- Seminar 3: Jessica Sommerville - Infants' Sensitivity to Cost and Benefit
- Lecture 3.5: Josh Tenenbaum - The Child as Scientist
- Unit 3 Debate: Tomer Ullman & Laura Schulz
- Lecture 4.1: Shimon Ullman - Development of Visual Concepts
- Lecture 4.2: Shimon Ullman - Atoms of Recognition
- Lecture 4.3: Aude Oliva - Predicting Visual Memory
- Seminar 4.1: Eero Simoncelli - Probing Sensory Representations
- Seminar 4.2: Amnon Shashua - Applications of Vision
- Lecture 5.1: Boris Katz - Vision and Language
- Lecture 5.2: Andrei Barbu - From Language to Vision and Back Again
- Lecture 5.3: Patrick Winston - Story Understanding
- Seminar 5: Tom Mitchell - Neural Representations of Language
- Lecture 6.1: Nancy Kanwisher - Introduction to Social Intelligence
- Lecture 6.2: Ken Nakayama - The Social Mind
- Lecture 6.3: Rebecca Saxe - MVPA: Window on the Mind via fMRI Part 1
- Lecture 6.4: Rebecca Saxe - MVPA: Window on the Mind via fMRI Part 2
- Lecture 7.1: Josh McDermott - Introduction to Audition Part 1
- Lecture 7.2: Josh McDermott - Introduction to Audition Part 2
- Lecture 7.3: Nancy Kanwisher - Human Auditory Cortex
- Lecture 7.4: Hynek Hermansky - Auditory Perception in Speech Technology Part 1
- Lecture 7.5: Hynek Hermansky - Auditory Perception in Speech Technology Part 2
- Unit 7 Panel: Vision and Audition
- Lecture 8.1: Russ Tedrake - MIT's Entry in the DARPA Robotics Challenge
- Lecture 8.2: John Leonard - Mapping, Localization, & Self-Driving Vehicles
- Lecture 8.3: Tony Prescott - Control Architecture in Mammals and Robots
- Lecture 8.4: Stefanie Tellex - Human-Robot Collaboration
- Lecture 8.5: Giorgio Metta - Introduction to the iCub Robot
- Lecture 8.6: iCub Team - Overview of Research on the iCub Robot
- Unit 8 Panel: Robotics
- Lecture 9.1: Tomaso Poggio - iTheory: Visual Cortex & Deep Networks
- Seminar 9: Surya Ganguli - Statistical Physics of Deep Learning
- Lecture 9.2: Haim Sompolinsky - Sensory Representations in Deep Networks
- Nick Cheney: Capturing Neural Plasticity in Deep Networks
- Danny Jeck: Impact of Attention on Cortical Models of Visual Recognition
- Alon Baram & Laurie Bayet: Learning to Recognize Digits and Faces from Few Examples
- David Rolnick & Ishita Dasgupta: Modeling Dynamic Memory with Hopfield Networks