Blog
The latest from Google Research
Natural Language in Voice Search
martes, 31 de julio de 2012
Posted by Jakob Uszkoreit, Software Engineer
On July 26 and 27, we held our eighth annual
Computer Science Faculty Summit
on our Mountain View Campus. During the event, we brought you a series of blog posts dedicated to sharing the Summit's talks, panels and sessions, and we continue with this glimpse into natural language in voice search. --Ed
At this year’s Faculty Summit, I had the opportunity to showcase the newest version of
Google Voice Search
. This version hints at how Google Search, in particular on mobile devices and by voice, will become increasingly capable of responding to natural language queries.
I first outlined the trajectory of Google Voice Search, which was initially released in 2007.
Voice actions
, launched in 2010 for Android devices, made it possible to control your device by speaking to it. For example, if you wanted to set your device alarm for 10:00 AM, you could say “set alarm for 10:00 AM. Label: meeting on voice actions.” To indicate the subject of the alarm, a meeting about voice actions, you would have to use the keyword “label”! Certainly not everyone would think to frame the requested action this way. What if you could speak to your device in a more natural way and have it understand you?
At last month’s
Google I/O 2012
, we announced a version of voice actions that supports much more natural commands. For instance, your device will now set an alarm if you say “my meeting is at 10:00 AM, remind me”. This makes even previously existing functionality, such as sending a text message or calling someone, more discoverable on the device -- that is, if you express a voice command in whatever way feels natural to you, whether it be “let David know I’ll be late via text” or “make sure I buy milk by 3 pm”, there is now a good chance that your device will respond how you anticipated it to.
I then discussed some of the possibly unexpected decisions we made when designing the system we now use for interpreting natural language queries or requests. For example, as you would expect from Google, our approach to interpreting natural language queries is data-driven and relies heavily on machine learning. In complex machine learning systems, however, it is often difficult to figure out the underlying cause for an error: after supplying them with training and test data, you merely obtain a set of metrics that hopefully give a reasonable indication about the system’s quality but they fail to provide an explanation for why a certain input lead to a given, possibly wrong output.
As a result, even understanding why some mistakes were made requires experts in the field and detailed analysis, rendering it nearly impossible to harness non-experts in analyzing and improving such systems. To avoid this, we aim to make every partial decision of the system as interpretable as possible. In many cases, any random speaker of English could look at its possibly erroneous behavior in response to some input and quickly identify the underlying issue - and in some cases even fix it!
We are especially interested in working with our academic colleagues on some of the many fascinating research and engineering challenges in building large-scale, yet interpretable natural language understanding systems and devising the machine learning algorithms this requires.
Etiquetas
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
materials science
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Responsible AI
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2021
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2020
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2019
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2018
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2017
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2016
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2015
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2014
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2013
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2012
dic
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2011
dic
nov
sep
ago
jul
jun
may
abr
mar
feb
ene
2010
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2009
dic
nov
ago
jul
jun
may
abr
mar
feb
ene
2008
dic
nov
oct
sep
jul
may
abr
mar
feb
2007
oct
sep
ago
jul
jun
feb
2006
dic
nov
sep
ago
jul
jun
abr
mar
feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
