What the DNN heard? Dissecting the machine brain for a better insight.

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• Khe Chai Sim, National University of Singapore
• Wednesday 04 November 2015, 12:00-13:00
• Department of Engineering - LR5.

If you have a question about this talk, please contact Anton Ragni.

Deep Neural Network (DNN) has been found to yield superior performance compared to the conventional Gaussian Mixture Model (GMM) based systems for automatic speech recognition. However, DNN has been used pretty much as a black box without much insights as to what the DNN has learned. This talk will present a novel approach for interpreting the DNN model, which is based on analysing the hidden activity pattern. This technique constructs a 2-dimensional hidden activity space where interpretable regions can be defined. This technique can be used to facilitate the understanding and comparison of the hidden activity patterns across different hidden layers, networks and time frames. Finally, a technique called “stimulated deep learning” will be presented, where phone stimuli are used to control the training process so that the hidden units of the resulting DNN yield interpretable activity patterns. Preliminary experimental results on TIMIT show that the proposed stimulated deep learning technique is able to learn DNNs with the hidden units showing phone-dependent activity regions without compromising the phone recognition performance.

This talk is part of the CUED Speech Group Seminars series.

This talk is included in these lists:

• CUED Speech Group Seminars
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge talks
• Chris Davis' list
• Department of Engineering - LR5
• Guy Emerson's list
• Information Engineering Division seminar list
• PhD related

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