Blog
The latest from Google Research
Automatic Captioning in YouTube
Friday, December 4, 2009
Posted by Christopher Alberti and Michiel Bacchiani, Google Research
On November 19, we launched our new
automatic captioning and automatic alignment feature
for YouTube. These features significantly reduce the effort it takes to create captions for videos on YouTube.
With YouTube expanding its index at a breakneck speed of
about 20 hours of new material uploaded each minute
, access to this vast body of video material becomes increasingly challenging. This is particularly true for people with hearing disabilities. A
2005 US census
showed that 7.8 million people (or about 3 percent of the US population) have difficulty hearing a normal conversation, with 1 million unable to hear at all. Hence, increased accesibility by adding captions to YouTube videos makes the corpus available to a much larger audience.
In addition to expanded accessibility for those with hearing disabilities, the combination of captions with
machine translation
expands YouTube accessibility across the globe. If a caption track is available, it can be translated automatically in any of the
51 currently available languages
. As a result, video content otherwise not accessible due to a language barrier can now be understood by a significantly larger user population.
Although captions are available in YouTube for hundreds of thousands of videos, it remains only a fraction of the the available corpus. Furthermore, only a tiny fraction of the avalanche of new video material getting uploaded is captioned. One reason for this lack of coverage is the effort it takes for a video uploader to generate captions. And this is where our new auto captioning and auto alignment features can benefit our uploaders. Auto-captioning uses automatic speech recognition technology to produce machine generated captions. Auto-alignment requires only a transcript--the uploader no longer has to sync that text with the video stream. To more concisely illustrate the use of these features, check out our
help center article
or this short video:
Modern-day speech recognition systems are big statistical machines trained on large sets of data. They do the best job recognizing speech in domains similar to their training data. Both the auto captioning and the auto alignment features use the speech recognition infrastructure that underlies Google Voice and Voice Search, but trained on different data. As an intial installment, for YouTube we use models trained on publicly available English broadcast news data. As a result, for now, the new features only work well on English material that is similar in style (i.e. an individual speaker who is speaking clearly).
The auto alignment features is available for all new video uploads, however the scope is limited to English material. The auto captioning feature is initially rolled out to a set of educational partners only. Although this is very limited in scope, the early launch makes the results of the system available to the viewers of this material instantly and it allows us to gauge early feedback which can aid in improving the features. We will release automatic captions more widely as quickly as possible.
Over time, we will work on improving the quality as well as the coverage of these features. Expansion will take place along two axes: additional languages will be made available and within each language we will cover much broader domains (beyond just broadcast news-like material). Since the content available in YouTube is so varied, it is difficult to set a timeline for this expansion. Automatic speech recognition remains challenging, in particular for the varied types of speech and background sounds and noise we see in the YouTube corpus. Therefore, to reach a high level of quality, we need to make advances in core technology. Although this will take time, we are committed to making that happen and to providing the larger community with the benefits of those developments.
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
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
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2020
Dec
Nov
Oct
Sep
Aug
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
.
