Xuedong Huang Chief Speech Scientist & Distinguished Engineer Microsoft Corporation
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1 Xuedong Huang Chief Speech Scientist & Distinguished Engineer Microsoft Corporation
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3 Cloud-enabled multimodal NUI with speech, gesture, gaze
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9 2015 System Human Error Rate 4%
10 Speech is a sequential process while FF-DNNs are not sequential in nature. FF-DNNs are not efficient in modeling temporal/spectral compression/stretch. (a) FF-DNN Theoretically, recurrent models should better model speech data. In RNNs, hidden representations are conditioned on all previously seen frames. (b) RNN 10
11 At each time step, each hidden node in an RNN consumes the current input vector and the previous hidden vector. x(t) h(t-1) h(t) (a) RNN node RNNs have a limited capacity to remember important distant past events. (Vanishing gradient problem) x(t) h(t-1) x(t) h(t-1) LSTM input, output, and forget gates combat vanishing gradient problem. x(t) h(t-1) h(t) x(t) h(t-1) (b) LSTM node 11
12 Models long-span phenomena well Good performance improvements observed But less convenient than fixed window methods. 12
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14 Machine learning enables nearly every value proposition of web search.
15 A personal assistant built around you Cortana gets to know you over time, building a relationship with you that s based on trust. She tracks the stuff you care about, looks out for you throughout the day, and helps filter out the noise so you can stay on top of what matters. Throughout, Cortana is delightful and easy to use.
16 PERSONAL Cortana LOOKS OUT FOR YOU Cortana is your truly personal assistant looks out for you gets to know you filters out the noise is transparent reminds you of what s important Proof points: Learning Proof points: Notebook Useful and relevant alerts Personal suggestions Planners Transparency & control Event scheduling Quiet hours and inner circle Reminders DELIGHTFUL & EASY TO USE Cortana just works lets you interact on your terms has a fun & engaging personality Proof points: Voice & natural language Text input Personality (visual, spoken voice, and behavior)
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19 HTTP: //
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21 CLIENT Application Logic Project Oxford API Engine Proxy Model Develop er Portal API Management Proxy RESTful API Façade Project Oxford API Application Logic Engine Proxy Model 21
22 News about flight delays 22
23 Train Deploy Access Learns models from a few examples Continuous refinement with active learning Existing Bing models for common cases 23
24 Model parameters are estimated with labeled data Labeling data is expensive Want to infer decision boundaries quickly No sense labeling this! Useful to label Cancel Book 24
25 Only label examples near the decision boundary [Dasgupta & Langford 2009] 25
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29 Coming soon
30 CNTK a flexible and open source deep learning toolkit Networks: CNN, RNN, Bidirectional LSTM, DSSM, CRF Problems: Speech, NLP, Ads, Search, large scale Learning algorithms: SGD, Adagrad, ADMM Network definition language Provides a simple yet powerful way to define a network CNTK simplifies deep learning experiments Design the model Derive the learning algorithm Implement the model Run the experiments
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