Blog
The latest from Google Research
Improving End-to-End Models For Speech Recognition
Thursday, December 14, 2017
Posted by Tara N. Sainath, Research Scientist, Speech Team and Yonghui Wu, Software Engineer, Google Brain Team
Traditional automatic speech recognition (ASR) systems, used for a variety of voice search applications at Google, are comprised of an acoustic model (AM), a pronunciation model (PM) and a language model (LM), all of which are independently trained, and often manually designed, on different datasets [1]. AMs take acoustic features and predict a set of subword units, typically context-dependent or context-independent phonemes. Next, a hand-designed lexicon (the PM) maps a sequence of
phonemes
produced by the acoustic model to words. Finally, the LM assigns probabilities to word sequences. Training independent components creates added complexities and is suboptimal compared to training all components jointly. Over the last several years, there has been a growing popularity in developing end-to-end systems, which attempt to learn these separate components jointly as a single system. While these end-to-end models have shown promising results in the literature [2, 3], it is not yet clear if such approaches can improve on current state-of-the-art conventional systems.
Today we are excited to share “
State-of-the-art Speech Recognition With Sequence-to-Sequence Models
[4],” which describes a new end-to-end model that surpasses the performance of a conventional production system [1]. We show that our end-to-end system achieves a
word error rate
(WER) of 5.6%, which corresponds to a 16% relative improvement over a strong conventional system which achieves a 6.7% WER. Additionally, the end-to-end model used to output the initial word hypothesis, before any hypothesis rescoring, is 18 times smaller than the conventional model, as it contains no separate LM and PM.
Our system builds on the Listen-Attend-Spell (LAS) end-to-end architecture, first presented in [2]. The LAS architecture consists of 3 components. The
listener
encoder component, which is similar to a standard AM, takes the a time-frequency representation of the input speech signal,
x
, and uses a set of neural network layers to map the input to a higher-level feature representation,
h
enc
. The output of the encoder is passed to an
attender
, which uses
h
enc
to learn an alignment between input features
x
and predicted subword units {y
n
, … y
0
}, where each subword is typically a
grapheme
or
wordpiece
. Finally, the output of the attention module is passed to the
speller
(i.e., decoder), similar to an LM, that produces a probability distribution over a set of hypothesized words.
Components of the LAS End-to-End Model.
All components of the LAS model are trained jointly as a single end-to-end neural network, instead of as separate modules like conventional systems, making it much simpler.
Additionally, because the LAS model is fully neural, there is no need for external, manually designed components such as finite state transducers, a lexicon, or text normalization modules. Finally, unlike conventional models, training end-to-end models does not require bootstrapping from decision trees or time alignments generated from a separate system, and can be trained given pairs of text transcripts and the corresponding acoustics.
In [4], we introduce a variety of novel structural improvements, including improving the attention vectors passed to the decoder and training with longer subword units (i.e., wordpieces). In addition, we also introduce numerous optimization improvements for training, including the use of minimum word error rate training [5]. These structural and optimization improvements are what accounts for obtaining the 16% relative improvement over the conventional model.
Another exciting potential application for this research is multi-dialect and multi-lingual systems, where the simplicity of optimizing a single neural network makes such a model very attractive. Here data for all dialects/languages can be combined to train one network, without the need for a separate AM, PM and LM for each dialect/language. We find that these models work well on 7 english dialects [6] and 9 Indian languages [7], while outperforming a model trained separately on each individual language/dialect.
While we are excited by our results, our work is not done. Currently, these models cannot process speech in real time [8, 9, 10], which is a strong requirement for latency-sensitive applications such as voice search. In addition, these models still compare negatively to production when evaluated on live production data. Furthermore, our end-to-end model is learned on 22 million audio-text pair utterances compared to a conventional system that is typically trained on significantly larger corpora. In addition, our proposed model is not able to learn proper spellings for rarely used words such as proper nouns, which is normally performed with a hand-designed PM. Our ongoing efforts are focused now on addressing these challenges.
Acknowledgements
This work was done as a strong collaborative effort between Google Brain and Speech teams. Contributors include Tara Sainath, Rohit Prabhavalkar, Bo Li, Kanishka Rao, Shankar Kumar, Shubham Toshniwal, Michiel Bacchiani and Johan Schalkwyk from the Speech team; as well as Yonghui Wu, Patrick Nguyen, Zhifeng Chen, Chung-cheng Chiu, Anjuli Kannan, Ron Weiss, Navdeep Jaitly, William Chan, Yu Zhang and Jan Chorowski from the Google Brain team. The work is described in more detail in papers [4-12].
References
[1] G. Pundak and T. N. Sainath, “
Lower Frame Rate Neural Network Acoustic Models
," in Proc. Interspeech, 2016.
[2] W. Chan, N. Jaitly, Q. V. Le, and O. Vinyals, “
Listen, attend and spell
,” CoRR, vol. abs/1508.01211, 2015
[3] R. Prabhavalkar, K. Rao, T. N. Sainath, B. Li, L. Johnson, and N. Jaitly, “
A Comparison of Sequence-to-sequence Models for Speech Recognition
,” in Proc. Interspeech, 2017.
[4] C.C. Chiu, T.N. Sainath, Y. Wu, R. Prabhavalkar, P. Nguyen, Z. Chen, A. Kannan, R.J. Weiss, K. Rao, K. Gonina, N. Jaitly, B. Li, J. Chorowski and M. Bacchiani, “
State-of-the-art Speech Recognition With Sequence-to-Sequence Models
,” submitted to ICASSP 2018.
[5] R. Prabhavalkar, T.N. Sainath, Y. Wu, P. Nguyen, Z. Chen, C.C. Chiu and A. Kannan, “
Minimum Word Error Rate Training for Attention-based Sequence-to-Sequence Models
,” submitted to ICASSP 2018.
[6] B. Li, T.N. Sainath, K. Sim, M. Bacchiani, E. Weinstein, P. Nguyen, Z. Chen, Y. Wu and K. Rao, “
Multi-Dialect Speech Recognition With a Single Sequence-to-Sequence Model
” submitted to ICASSP 2018.
[7] S. Toshniwal, T.N. Sainath, R.J. Weiss, B. Li, P. Moreno, E. Weinstein and K. Rao, “
End-to-End Multilingual Speech Recognition using Encoder-Decoder Models
”, submitted to ICASSP 2018.
[8] T.N. Sainath, C.C. Chiu, R. Prabhavalkar, A. Kannan, Y. Wu, P. Nguyen and Z. Chen, “
Improving the Performance of Online Neural Transducer Models
”, submitted to ICASSP 2018.
[9] C.C. Chiu* and C. Raffel*, “
Monotonic Chunkwise Attention
,” submitted to ICLR 2018.
[10] D. Lawson*, C.C. Chiu*, G. Tucker*, C. Raffel, K. Swersky, N. Jaitly. “
Learning Hard Alignments with Variational Inference
”, submitted to ICASSP 2018.
[11] T.N. Sainath, R. Prabhavalkar, S. Kumar, S. Lee, A. Kannan, D. Rybach, V. Schogol, P. Nguyen, B. Li, Y. Wu, Z. Chen and C.C. Chiu, “
No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models
,” submitted to ICASSP 2018.
[12] A. Kannan, Y. Wu, P. Nguyen, T.N. Sainath, Z. Chen and R. Prabhavalkar. “
An Analysis of Incorporating an External Language Model into a Sequence-to-Sequence Model
,” submitted to ICASSP 2018.
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
Biology
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
2022
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
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
.
