Blog
The latest news from Google AI
Evaluation of Speech for the Google Assistant
Thursday, December 21, 2017
Posted by Enrique Alfonseca, Staff Research Scientist, Google Assistant
Voice interactions with technology are becoming a key part of our lives — from asking your phone for traffic conditions to work to using a smart device at home to turn on the lights or play music. The Google Assistant is designed to provide help and information across a variety of platforms, and is built to bring together a number of products — including Google Maps, Search, Google Photos, third party services, and more. For some of these products, we have released specific evaluation guidelines, like
Search Quality Rating Guidelines
. However, the Google Assistant needs its own guidelines in place, as many of its interactions utilize what is called “eyes-free technology,” when there is no screen as part of the experience.
In the past we have received requests to see our evaluation guidelines from academics who are researching improvements in voice interactions, question answering and voice-guided exploration. To facilitate their evaluations, we are
publishing some of the first Google Assistant guidelines
. It is our hope that making these guidelines public will help the research community build and evaluate their own systems.
Creating the Guidelines
For many queries, responses are presented on the display (like a phone) with a graph, a table, or an interactive element, like you’d see for [
weather this weekend
].
But spoken responses are very different from display results, as what’s on screen needs to be translated into useful speech. Furthermore, the contents of the voice response are sometimes sourced from the web, and in those cases it’s important to provide the user with a link to the original source. While users looking at their mobile device can click through to read the original web page, an eyes free solution presents unique challenges. In order to generate the optimal audio response, we use a combination of
explicit linguistic knowledge and deep learning solutions
that allow us to keep answers grammatical, fluent and concise.
How do we ensure that we consistently meet user expectations on quality, across all answer types and languages? One of the tools we use to measure that are human evaluations. In these, we ask raters to make sure that answers are satisfactory across several dimensions:
Information Satisfaction:
the content of the answer should meet the information needs of the user.
Length:
when a displayed answer is too long, users can quickly scan it visually and locate the relevant information. For voice answers, that is not possible. It is much more important to ensure that we provide a helpful amount of information, hopefully not too much or too little. Some of
our previous work
is currently in use for identifying the most relevant fragments of answers.
Formulation:
it is much easier to understand a badly formulated written answer than an ungrammatical spoken answer, so more care has to be placed in ensuring grammatical correctness.
Elocution:
spoken answers must have proper pronunciation and prosody. Improvements in text-to-speech generation, such as
WaveNet
and
Tacotron 2
, are quickly reducing the gap with human performance.
The current version of the guidelines can be found
here
. Of course, guidelines are often updated, and these are just a snapshot of something that is a living, changing, always-work-in-progress evaluation!
Labels
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
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
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
2020
Jul
Jun
May
Apr
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
