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gPU -ACCELERATED hmm FOR Speech RecognitionPowerPoint Presentation

gPU -ACCELERATED hmm FOR Speech Recognition

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### gPU-ACCELERATED hmm FOR Speech Recognition

Leiming Yu, YashUkidave and David KaeliECE, Northeastern University

Outline

- Background & Motivation
- HMM
- GPGPU
- Results
- Future Work

Background

- Translate Speech to Text
- Speaker DependentSpeaker Independent
- Applications* Natural Language Processing * Home Automation * In-car Voice Control * Speaker Verifications * Automated Banking * Personal Intelligent Assistants Apple Siri Samsung S Voice * etc.

[http://www.kecl.ntt.co.jp]

DTW

Dynamic Time WarpingA template-based approach to measure similarity between two temporal sequences which may vary in time or speed.

[opticalengineering.spiedigitallibrary.org]

DTW

Dynamic Time Warping

DTW Pros:

1) Handle timing variation

2) Recognize Speech at reasonable cost

DTW Cons:

1) Template Choosing

2) Ending point detection (VAD, acoustic noise)

3) Words with weak fricatives, close to acoustic background

For i := 1 to n

For j := 1 to m

cost:= D(s[i], t[j])

DTW[i, j] := cost + minimum(DTW[i-1, j ],

DTW[i, j-1],

DTW[i-1, j-1])

Neural Networks

Algorithms mimics the brain.

Simplified Interpretation: * takes a set of input features * goes through a set of hidden layers * produces the posterior probabilities as the output

Neural Networks

Parking Meter

Bike

Pedestrian

Car

If Pedestrian

“activation” of unit in layer

matrix of weights controlling function mapping from layer to layer

[Machine Learning, Coursera]

Neural Networks

Equation Example

Neural Networks Example

Hint: * effective in recognizing individual phones

isolated words as short-time units

* not ideal for continuous recognition tasks largely due to the poor ability to model temporal dependencies.

Hidden Markov Model

In a Hidden Markov Model, * the states are hidden * output that depend on the states are visible

x — states

y — possible observations

a — state transition probabilities

b — output probabilities

[wikipedia]

Hidden Markov Model

The temporal transition of the hidden states fits well with the nature of phoneme transition.

Hint: * Handle temporal variability of speech well

* Gaussian mixture models(GMMs), controlled by the hidden variables determine how well a HMM can represent the acoustic input. * Hybrid with NN to leverage each modeling technique

Motivation

- Parallel Architecturemulti-core CPU to many-core GPU ( graphics + general purpose)
- Massive Parallelism in Speech Recognition SystemNeural Networks, HMMs, etc. , are both Computation and Memory Intensive
- GPGPU Evolvement * Dynamic Parallelism
- * Concurrent Kernel Execution * Hyper-Q
- * Device Partitioning * Virtual Memory Addressing * GPU-GPU Data Transfer, etc.
- Previous works
- Our goal is to use new modern GPU features to accelerate Speech Recognition

Outline

- Background & Motivation
- HMM
- GPGPU
- Results
- Future Work

Hidden Markov Model

Markov chains and processes are named after Andrey Andreyevich Markov(1856-1922), a Russian mathematician, whose Doctoral Advisor is PafnutyChebyshev.

1966, Leonard Baum described the underlying mathematical theory.

1989, Lawrence Rabiner wrote a paper with the most comprehensive description on it.

Hidden Markov Model

HMM Stages

* causal transitional probabilities between states

* observation depends on current state, not predecessor

Hidden Markov Model

- Forward
- Backward
- Expectation-Maximization

Hidden Markov Model

- Forward
- Backward
- Expectation-Maximization

HMM-EM

Variable Definitions:

* Initial Probability

* Transition Prob. Observation Prob.

* Forward Variable Backward Variable

Other Variables During Estimation:

* theestimated state transition probability matrix, epsilon

* the estimated probability in a particular state at time t, gamma

* Multivariate Normal Probability Density Function

Update Obs. Prob. From Gaussian Mixture Models

Outline

- Background & Motivation
- HMM
- GPGPU
- Results
- Future Work

GPGPU

Programming Model

GPGPU

GPU-powered Eco System

1) Programming Model

* CUDA

* OpenCL

* OpenACC, etc.

2) High Performance Libraries

* cuBLAS

* Thrust

* MAGMA (CUDA/OpenCL/Intel Xeon Phi)

* Armadilo (C++ Linear Algebra Library), drop-in libraries etc.

3) Tuning/Profiling Tools

* Nvidia: nvprof / nvvp * AMD: CodeXL

4) Consortium Standards

Heterogeneous System Architecture (HSA) Foundation

Outline

- Background& Motivation
- HMM
- GPGPU
- Results
- Future Work

Results

Platform Specs

Results

Mitigate Data Transfer Latency

Pinned Memory Size current process limit: ulimit -l ( in KB )

hardware limit: ulimit –H –l

increase the limit: ulimit –S –l 16384

Results

A Practice to Efficiently Utilize Memory System

Results

Hyper-Q Feature

Outline

- Background& Motivation
- HMM
- GPGPU
- Results
- Future Work

Future Work

- Integrate with Parallel Feature Extraction
- Power Efficiency Implementation and Analysis
- Embedded System Development, Jetson TK1 etc.
- Improve generosity, LMs
- Improve robustness, Front-end noise cancelation
- Go with the trend!

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