Machine learning has ignited the voice UI and virtual assistant revolution as machine speech recognition approaches the accuracy of humans. The AI powering key voice UI components, such as automatic speech recognition and natural language processing, has traditionally run in the cloud due to computing, storage, and power constraints. However, on-device processing of voice UI provides unique benefits, such as instant response, reliability, and privacy. And fusing multiple on-device sensor inputs, such as camera and accelerometers, in addition to microphones adds a level of personalization that will take us closer to a true personal assistant.

1. Making an on-device
personal assistant a reality
Qualcomm Technologies, Inc.
@qualcomm_techJune 2018

2. 2
Reasoning
Learn, infer context,
and anticipate
Perception
Hear, see, and
observe
Action
Act intuitively, interact
naturally, and protect
privacy
AI brings human-like
understanding and
behaviors to the
machines

3. 3
Advancing AI research to make on-device AI ubiquitous
A common platform is fundamental to scaling AI internally and across the industry
Power efficiency
Model design, compression, quantization,
activation, algorithms, and efficient hardware
Efficient learning
Robust learning through minimal data,
unsupervised learning, and on-device learning
Personalization
Continuous learning, model adaptation,
and privacy-preserved distributed learning
System architecture
Multi-task and multi-modal learning, sensor fusion, and cloud-edge systems
Action
Reinforcement learning
for decision making
Reasoning
Scene understanding, language
understanding, behavior prediction
Perception
Object detection, speech
recognition, contextual fusion
AutomotiveIoT Mobile

4. 4
A true personal assistant
One of many use cases requiring a broad set of AI capabilities
Power efficiency
Model design, compression, quantization,
activation, algorithms, and efficient hardware
Efficient learning
Robust learning through minimal data,
unsupervised learning, and on-device learning
Personalization
Continuous learning, model adaptation,
and privacy-preserved distributed learning
AutomotiveIoT Mobile
Action
Reinforcement learning
for decision making
Reasoning
Scene understanding, language
understanding, behavior prediction
Perception
Object detection, speech
recognition, contextual fusion
System architecture
Multi-task and multi-modal learning, sensor fusion, and cloud-edge systems

5. 5
v
Designed to be:
Always-on
Conversational
Personal
Private
Critical to create a
true virtual assistant
Voice is the
transformative user
interface (UI) we’ve
been waiting for

6. 6
Voice UI components required for an end-to-end solution
Text-to-speech
Speech
synthesis
Natural language generation
Dialog management
Natural language understanding
Natural
language
processing
Speech-to-text
Speech
recognition
“Alexa,” “Hey Snapdragon”
Always-on keyword detection
Voice
activation
Echo cancellation
Speech denoising
Speech
pre-processing
Signal acquisition and playback
Front-end
processing
Machine speech chain: listener and speaker
Qualcomm Snapdragon is a product of Qualcomm Technologies, Inc. and/or its subsidiaries.

7. 77
Machine learning has ignited the voice UI revolution
“As speech recognition accuracy goes from say 95% to 99%, all of us in the room will go from barely using it today to using it all
the time. Most people underestimate the difference between 95% and 99% accuracy— 99% is a gamechanger. No one wants
to wait 10 seconds for a response. Accuracy, followed by latency, are the two key metrics for a production speech system.”
— Andrew Ng
GMM: Gaussian Mixture Model, CNN: Convolutional Neural Network, RNN: Recurrent Neural Network
Human accuracy
GMM RNN + CNN
Machine automatic speech recognition accuracy
202020102000199019801970
50%
CNN
55%
60% 62%
70%
95%

8. 8
TV
Smart
speakers
Headphones
and headsets
IoT
Smartphones
and tablets
In-car
entertainment
systems
PCs and
laptops
Wearables
XR
Voice UI is proliferating
across product categories

9. 9
Moving voice UI functionality to the end device
An end-to-end solution powered by machine learning
Automatic speech
recognition
(ASR)
Text-to-speech
(TTS)
News
SMS
Music
Maps Wikipedia
Weather Stocks
Voice
activation
Service manager
Multi-mic echo
cancellation,
beamforming,
and speech
denoising
Natural language
understanding
(NLU)
On-device processing
(always-on and real-time)
Cloud processing
(services)
Cloud centric (today)

10. 10
Moving voice UI functionality to the end device
An end-to-end solution powered by machine learning
Automatic speech
recognition
(ASR)
Text-to-speech
(TTS)
News
SMS
Music
Maps Wikipedia
Weather Stocks
Voice
activation
Service manager
Multi-mic echo
cancellation,
beamforming,
and speech
denoising
Natural language
understanding
(NLU)
On-device processing (always-on and real-time) Cloud processing (services)
On-device centric (future)

11. 11
Cloud tasks
Complex voice fallback
Training and model update
Knowledge base
Services
On-device
processing
of voice UI
Provides unique benefits
complementing the cloud
Challenge
Providing the voice UI functionality
within the power/thermal envelope
Machine learning models
Offline raw data
Queries
On-device tasks
Automatic speech recognition
Natural language processing
Always-on audio cognition
On-device training
Benefits
Privacy
Instant response
Always-on
Device context

12. 12
Noisy speech spectrogram
Clean speech spectrogram
“If people were more generous,
there would be no need for welfare”
0.5 1 1.5 2 2.5 3
4000
3000
2000
1000
0
Frequency
Time
0.5 1 1.5 2 2.5 3
4000
3000
2000
1000
0
Frequency
Time
DL-based denoising model
trained with extensive speech
noise databases
DL-based
denoising
Speech
denoising
• Single or multiple mics
• Applicable for
◦ Two-way conversation
◦ Voice/speaker recognition
◦ Keyword spotting
• Deep learning (DL)
significantly improves the
performance over traditional
methods
• Robust in challenging
interference and noise
scenarios
“If people were more generous,
there would be no need for welfare”

13. 13
Qualcomm
Voice
Activation
supports:
Qualcomm® Voice
Activation (VA)
High accuracy, robust to background
noise, and supports multiple languages
Deep learning is improving performance
Among state-of-the-art in terms of
performance vs. power consumption
-47%
-11%
2014 2015 2016 2017
QualcommVApowerconsumption
WCD9330
WCD9335
WCD9340
Amazon Alexa
Baidu DUEROS
Microsoft Cortana
Google Assistant
Qualcomm Voice Activation, Qualcomm WCD9330, Qualcomm WCD9335, and Qualcomm WCD9340 are products of Qualcomm Technologies, Inc. and/or its subsidiaries.

14. 14
Transcribe the
audio to text
Deep learning gives
state-of-the-art accuracy
on a mobile device
Personalization—adaptation
to individual accent and
acoustic environment
Automatic
speech
recognition
Natural language
understanding (NLU)
Acoustic features Acoustic model
Reduce input audio to
essential information
Language model
Deep learning converts
input into linguistic units
Adapted to each user’s
accent and environment
Uses context and language
statistics for best utterance
estimation
Adapted to each user’s
speaking tendencies
Allows the same intention
to be expressed in multiple
ways
Adapted to each user’s
intent expressions
“Turn  on  the  light”
On-device automatic speech recognition (ASR)
User intention

15. 1515
An end-to-end on-device voice UI example for smart homes
99% on-device intent accuracy
is achieved for domain specific command sets when adapted to accent and environmental condition
Demo of automatic speech recognition and natural language understanding
Large command set
Turn on the living room lights
Click the kitchen lights off
Turn off all lights
Switch on the ceiling fan
Shut off the sprinklers
Start music
Pause song
Next track
Go back one
Play previous song
Turn speaker off
Increase temperature
Intent understanding
Turn on the kitchen light
Click kitchen light on
Switch on light in the kitchen
Turn the light on in the kitchen
NLU: These four phrases
map to the same intent

16. 16
A true virtual
assistant
A “digital me” sitting on the device:
context aware and personalized

17. 1717
Calendar
Messaging
Apps
On-device data
Contextual intelligence is required for personalization
The fusion of many types of sensors and personal information
17
Low power sensing, processing, and connectivity
Efficient, heterogeneous
architectures
Sensor fusion and
machine learning
Integrated, always-on
data capturing
Low-energy wireless technologies
(e.g. BT-LE, 5G NR IoT)
Cloud data
Off-device data
IoT data
Sensor fusion
Gyroscope
Compass
Camera
Ambient light
Temperature
Iris scanEnvironment
Pulse
HumidityMicrophone
Sensor data
C-V2X

18. 1818
Creating personalized memories
Essential for a true virtual assistant
History, number
of people, identity
After the party, strolling on the
beach at sunset in La Jolla talking
with my son and laughing
Live sentiment
analysis
Strolling on the beach at sunset
in La Jolla talking with my son
and laughing
Activity analysis
Strolling on the beach at sunset
in La Jolla talking with my son
GPS location
La Jolla, California
Visual analysis
A sunset over the ocean
in La Jolla
Sound analysis
Talking with my son at sunset in
La Jolla

19. 19
A true personal assistant is responsive and proactive
“Remember the time I was strolling with
my son after the party at La Jolla beach?”
“Yes I do, here is a picture you took of the sunset.
Should I share it with your family group on WeChat?”
Responsive
Decision-making and conversation based
on contextual analysis and prompting
(e.g. finding memories)
Proactive
Decision-making and conversation based
on contextual analysis without prompting
(e.g. automatically sharing memories)
“I noticed that you are tired and stressed, I’m turning
on the Rocky III soundtrack and navigating you
to the gym for a workout and sauna.”
“This music gets my blood going and a
workout and sauna will help me relieve stress.”

20. 20
Multi-mic echo
cancellation,
beamforming,
and speech
denoising
The first step to an on-device virtual assistant
Enabling on-device voice UI
News
SMS
Music
Maps Wikipedia
Weather Stocks
Voice
activation
Service manager
Automatic speech
recognition (ASR)
Natural language
understanding (NLU)
On-device processing Cloud processing
Text-to-speech
(TTS)

21. 21
Adding an “AI agent” to create a true virtual assistant
The on-device AI agent continuously learns personal knowledge and acts intuitively
On-device processing Cloud processing (services)
Cloud knowledge graph
Automatic speech
recognition (ASR)
Multi-mic echo
cancellation and
beamforming
Sensors
Text-to-speech
(TTS)
Natural language
understanding (NLU)
Voice
activation
AI agent

22. 22
Adding an “AI agent” to create a true virtual assistant
Contextualization allows personalization at acoustic, intent, and behavior levels
Cloud processing (services)
Automatic speech
recognition (ASR)
Voice
activation
Multi-mic echo
cancellation and
beamforming
Cloud knowledge
graph
On-device processing
Text-to-speech
(TTS)
Sensors
Natural language
understanding (NLU)
AI agent
Dialog
management
Speaker identification
Acoustic event detection
Gender and age detection
Voice activity detection
Emotion classification
Contextual fusion
and learning
Local knowledge
graph

23. 23
Various kitchen
noise samples
Posterior-gram
ML-based
acoustic event
classification
Object rustling
Object snapping
Cupboard
Cutlery
Dishes
Drawer
Glass jingling
Object impact
Walking
Washing dishes
Water tap running
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50
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350
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450
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0.1
Acoustic
event
detection
• ML techniques
are used to
◦ Classify acoustic
signals into a set of
predefined events
◦ Infer acoustic environment
• Low power,
always-on

24. 2424
We are advancing AI research
to make on-device AI ubiquitous
We are creating AI platform
innovations that are fundamental
to scaling AI across the industry
We provide the low-power
end-to-end on-device solution
for a true personal assistant

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www.qualcomm.com & www.qualcomm.com/blog
Thank you!
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