Blog
The latest news from Google AI
The neural networks behind Google Voice transcription
Tuesday, August 11, 2015
Posted by Françoise Beaufays, Research Scientist
Over the past several years,
deep learning
has shown remarkable success on some of the world’s most difficult computer science challenges, from
image classification and captioning
to
translation
to
model visualization techniques
. Recently
we announced
improvements to Google Voice transcription using
Long Short-term Memory Recurrent Neural Networks
(LSTM RNNs)—yet another place neural networks are improving useful services. We thought we’d give a little more detail on how we did this.
Since it launched in 2009, Google Voice transcription had used
Gaussian Mixture Model
(GMM) acoustic models, the state of the art in speech recognition for 30+ years. Sophisticated techniques like
adapting the models
to the speaker's voice augmented this relatively simple modeling method.
Then around 2012, Deep Neural Networks (DNNs)
revolutionized the field of speech recognition
. These multi-layer networks distinguish sounds better than GMMs by using “discriminative training,”
differentiating phonetic units
instead of modeling each one independently.
But things really improved rapidly with Recurrent Neural Networks (RNNs), and especially LSTM RNNs,
first launched
in Android’s speech recognizer in May 2012. Compared to DNNs, LSTM RNNs have additional recurrent connections and memory cells that allow them to “remember” the data they’ve seen so far—much as you interpret the words you hear based on previous words in a sentence.
By then, Google’s old voicemail system, still using GMMs, was far behind the new state of the art. So we decided to rebuild it from scratch, taking advantage of the successes demonstrated by LSTM RNNs. But there were some challenges.
An LSTM memory cell, showing the gating mechanisms that allow it to store
and communicate information. Image credit: Jürgen Schmidhuber
There’s more to speech recognition than recognizing individual sounds in the audio: sequences of sounds need to match existing words, and sequences of words should make sense in the language. This is called “language modeling.” Language models are typically trained over very large corpora of text, often orders of magnitude larger than the acoustic data. It’s easy to find lots of text, but not so easy to find sources that match naturally spoken sentences. Shakespeare’s plays in 17th-century English won’t help on voicemails.
We decided to retrain both the acoustic and language models, and to do so using existing voicemails. We already had a small set of voicemails users had donated for research purposes and that we could transcribe for training and testing, but we needed much more data to retrain the language models. So we asked our users to donate their voicemails in bulk, with the assurance that the messages wouldn’t be looked at or listened to by anyone—only to be used by computers running machine learning algorithms. But how does one train models from data that’s never been human-validated or hand-transcribed?
We couldn’t just use our old transcriptions, because they were already tainted with recognition errors—
garbage in, garbage out
. Instead, we developed a delicate iterative pipeline to retrain the models. Using improved acoustic models, we could recognize existing voicemails offline to get newer, better transcriptions the language models could be retrained on, and with better language models we could recognize again the same data, and repeat the process. Step by step, the recognition error rate dropped, finally settling at roughly half what it was with the original system! That was an excellent surprise.
There were other (not so positive) surprises too. For example, sometimes the recognizer would skip entire audio segments; it felt as if it was falling asleep and waking up a few seconds later. It turned out that the acoustic model would occasionally get into a “bad state” where it would think the user was not speaking anymore and what it heard was just noise, so it stopped outputting words. When we retrained on that same data, we’d think all those spoken sounds should indeed be ignored, reinforcing that the model should do it even more. It took careful tuning to get the recognizer out of that state of mind.
It was also tough to get punctuation right. The old system relied on hand-crafted rules or “grammars,” which, by design, can’t easily take textual context into account. For example, in an early test our algorithms transcribed the audio “I got the message you left me” as “I got the message. You left me.” To try and tackle this, we again tapped into neural networks, teaching an LSTM to insert punctuation at the right spots. It’s still not perfect, but we’re continually working on ways to improve our accuracy.
In speech recognition as in many other complex services, neural networks are rapidly replacing previous technologies. There’s always room for improvement of course, and we’re already working on new types of networks that show even more promise!
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
2021
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
.
