How Microsoft Had Made Deep Learning Red-Hot in IT IndustryZhijie Yan, Microsoft Research Asia

USTC visit, May 6, 2014

Self Introduction

@MSRA 鄢志杰 996 – studied in USTC from 1999 to 2008

Graduate student – studied in iFlytek speech lab from 2003 to 2008, supervised by Prof. Renhua Wang

Intern – worked in MSR Asia from 2005 to 2006

Visiting scholar – visited Georgia Tech in 2007

FTE – worked in MSR Asia since 2008

Research interests

Speech, deep learning, large-scale machine learning

In Today’s Talk

Deep learning becomes very hot in the past few years

How Microsoft had made deep learning hot in IT industry

Deep learning basics

Why Microsoft can turn all these ideas into reality

Further reading materials

How Hot is Deep Learning

“This announcement comes on the heels of a $600,000 gift Google awarded Professor Hinton’s research group to support further work in the area of neural nets.” – U. of T. website

How Hot is Deep Learning

How Hot is Deep Learning

How Hot is Deep Learning

How Hot is Deep Learning

Microsoft Had Made Deep Learning Hot in IT Industry

Initial attempts made by University of Toronto had shown promising results using DL in speech recognition on TIMIT phone recognition task

Prof. Hinton’s student visited MSR as an intern, good results were obtained on Microsoft Bing voice search task

MSR Asia and Redmond collaborated and got amazing results on Switchboard task, which shocked the whole industry

Microsoft Had Made Deep Learning Hot in IT Industry

*figure borrowed from MSR principal researcher Li DENG

Microsoft Had Made Deep Learning Hot in IT Industry

Followed by others and results were confirmed in various different speech recognition tasks

Google / IBM / Apple / Nuance / 百度 / 讯飞 Continuously advanced by MSR and others

Expand to solve more and more problems

Image processing

Natural language processing

Search

…

Deep Learning From Speech to Image

ILSVRC-2012 competition on ImageNet

Classification task: classify an image into 1 of the 1,000 classes in your 5 bets

airliner lifeboat school bus

Institution Error rate (%)

University of Amsterdam 29.6

XRCE/INRIA 27.1

Oxford 27.0

ISI 26.2

Deep Learning From Speech to Image

ILSVRC-2012 competition on ImageNet

Classification task: classify an image into 1 of the 1,000 classes in your 5 bets

airliner lifeboat school bus

Institution Error rate (%)

University of Amsterdam 29.6

XRCE/INRIA 27.1

Oxford 27.0

ISI 26.2

SuperVision 16.4

Deep Learning Basics

Deep learning deep neural networks multi-layer perceptron (MLP) with a deep structure (many hidden layers)

Input layer

Hidden layer

Output layer

W0

W1

Input layer

Hidden layer

Output layer

W0

W1

Hidden layer

W2

Hidden layer

W3

Deep Learning Basics

Sounds not new at all? Sounds familiar like you’ve learned in class?

Things not change over the years

Network topology / activation functions / …

Backpropagation (BP)

Things changed recently

Data Big data

General-purpose computing on graphics processing units (GPGPU)

“A bag of tricks” accumulated over the years

E.g. Deep Neural Network for Speech Recognition

Three key components that make DNN-HMM work

Tied tri-phones as

the basis units for HMM states

Many layers of nonlinear

feature transformatio

n Long window of frames

*figure borrowed from MSR senior researcher

Dong YU

E.g. Deep Neural Network for Image Classification

The ILSVRC-2012 winning solution

*figure copied from Krizhevsky, et al., “ImageNet Classification with Deep Convolutional Neural Networks”

Scale Out Deep Leaning

Training speed was a major problem of DL

Speech recognition model trained with 1,800-hour data (~650,000,000 vector frames) costs 2 weeks using 1 GPU

Image classification model trained with ~1,000,000 figures costs 1 weeks using 2 GPUs*

How to scale out if 10x, 100x training data becomes available?

*Krizhevsky, et al., “ImageNet Classification with Deep Convolutional Neural Networks”

DNN-GMM-HMM

Joint work with USTC-MSRA Ph.D. program student, Jian XU ( 许健 , 0510)

The “DNN-GMM-HMM” approach for speech recognition*

DNN as hierarchical nonlinear feature extractor, trained using a sub-set of training data

GMM-HMM as acoustic model, trained using full data

*Z.-J. Yan, Q. Huo, and J. Xu, “A scalable approach to using DNN-derived features in GMM-HMM based acoustic modeling for LVCSR”

DNN-GMM-HMM

DNN-deriv

ed features

PCA HLDA

Tied-state WE-RDLT

MMI sequence traini

ng

CMLLR

unsupervised

adaptation

GMM-HMM modeling of DNN-derived features: combine the best of both worlds

Experimental Results 300hr DNN (18k states, 7 hidden layers) + 2,000hr

GMM-HMM (18k states)*

Training time reduced from 2 weeks to 3-5 days

10

11

12

13

14

15 15.414.7

13.813.1

Word Error Rate (%)

DNN-HMM (CE) DNN-GMM-HMM (RDLT)DNN-GMM-HMM (MMI) DNN-GMM-HMM (UA)

10% WERR15% WERR

*Z.-J. Yan, Q. Huo, and J. Xu, “A scalable approach to using DNN-derived features in GMM-HMM based acoustic modeling for LVCSR”

A New Optimization Method

Joint work with USTC-MSRA Ph.D. program student, Kai Chen ( 陈凯 , 0700)

Using 20 GPUs, time needed to train a 1,800-hour acoustic model is cut from 2 weeks to 12 hours, without accuracy loss

The magic is to be published

We believe the scalability issue in DNN training for speech recognition is now solved!

Why Microsoft Can Do All These Good Things Research

Bridge the gap between academia and industry via our intern and visiting scholar programs

Scale out from toy problems to real-world industry-scale applications

Product team

Solve practical issues and deploy technologies to serve users worldwide via our services

All together

We continuously improve our work towards larger scale, higher accuracy, and to tackle more challenging tasks

Finally

We have big-data + world-leading computational infrastructure

If You Want to Know More About Deep Learning

Neural networks for machine learning: https://class.coursera.org/neuralnets-2012-001

Prof. Hinton’s homepage: http://www.cs.toronto.edu/~hinton/

DeepLearning.net: http://deeplearning.net/

Open-source

Kaldi (speech): http://kaldi.sourceforge.net/

cuda-convent (image): http://code.google.com/p/cuda-convnet/

Thanks!