Blog
The latest news from Google AI
Text-to-Speech for Low-Resource Languages (Episode 3): But can it say “Google”?
Friday, February 19, 2016
Posted by Martin Jansche, Software Engineer, Google Research for Low Resource Languages
This is the third episode in the series of posts reporting on the work we are doing to build text-to-speech (TTS) systems for low resource languages. In the
first episode
, we described the crowdsourced acoustic data collection effort for Project Unison. In the
second episode
, we described how we built parametric voices based on that data. In this episode, we look at how we are compiling a pronunciation lexicon for a TTS system.
In Project Unison we are developing ways to bring Google's spoken language technology to the world’s major languages. As part of this broader goal, we are piloting a process for building a text-to-speech (TTS) system that can speak Bengali (Bangla). While our exploration of new methods have allowed us to
gather sufficient data
to
train a statistical parametric voice
capable of speaking Bengali, we had to address the next challenge: How do we make the voice sound like it is fluent in that language?
When people learn foreign languages, they are usually expected to pick up the full details from repeated exposure once they've mastered the basics and reached sufficient fluency. Often second-language learners struggle with issues that may seem so natural to fluent speakers that they are taken for granted. For instance, in order to read text out loud, one must know how to read different kinds of numerical expressions (e.g. dates, times, phone numbers, Roman numerals), and how to pronounce a wide variety of words, ranging from native words to newly coined brand names to loanwords, which themselves can arrive from different source languages. As TTS systems heavily rely on machine learning, they tend to face similar challenges as human learners: the way words are pronounced is often complex, sometimes surprising, and rarely fully documented.
Take the Bengali word meaning "microscope", which is অণুবীক্ষণ. Its pronunciation can be transcribed in the
International Phonetic Alphabet
as /o.nu.bik.kʰɔn/. When our system encounters this word, it analyzes the spelling in
Bengali script
into abstract written units called graphemes and then predicts the spoken sounds, or phonemes, of
Bengali phonology
from these graphemes.
The correspondence between graphemes and phonemes varies along several dimensions. One dimension is horizontal complexity: in many cases a single grapheme corresponds to a single phoneme, but the Bengali ligature ক্ষ is special, as several graphemes correspond to several phonemes in a somewhat surprising way. Another dimension is vertical predictability: a grapheme may correspond to different phonemes in different contexts and the correct phoneme may be difficult to predict. The Bengali grapheme “a” / “-a” is both very frequent and its pronunciation very unpredictable. It either corresponds to the phoneme /o/ or to the phoneme /ɔ/ or it is not pronounced. In the table above, we see all three possibilities within one word. As is standard in speech processing, our approach relies on human experts who transcribe words into phoneme sequences and machine learning models that capture the complex aspect of the grapheme-phoneme correspondence.
In order for our Bengali TTS system to pronounce the words in a sentence, it relies on a pronunciation dictionary, or lexicon, that provides pronunciations of a number of common words. When a word is not in the lexicon, it falls back on a machine learning model that was trained on thousands of pronunciations, which can then provide a pretty good guess at how a previously unseen word is pronounced. With a sufficiently large pronunciation dictionary, the system can be expected to reach a high level of fluency.
We first started compiling a Bengali pronunciation lexicon, with our Bangladeshi linguists transcribing a few thousand words into phonemes. This work was done in a web application that had been custom built for this purpose. Just like
an earlier version
, this transcription tool supports the work of linguists by providing a virtual keyboard for entering phonemes.
Once a few thousand words had been transcribed, we trained a machine learning system that could predict phonemic transcriptions for previously unseen words, so that the linguists only had to correct the output of that system. After the TTS voice had been built, it also became possible to listen to the voice reading out the entered transcriptions.
Even before the first machine learning model had been trained for Bengali, we configured the transcription tool to provide some constraints on how words could be transcribed. Bengali, like most writing systems, has certain aspects that make it complex, while in other ways it is quite regular. As discussed above, the grapheme “a” (অ) can have different pronunciations depending on context, but its pronunciation does not vary wildly: it is either silent or pronounced as a vowel, never as a consonant. By incorporating constraints on which graphemes can correspond to which phonemes, we can easily identify unlikely or erroneous transcriptions. This methodology has been
in use at Google for several years
.
The grapheme-phoneme correspondence varies along several dimensions, including regular words vs. abbreviations, and native words vs. loanwords. For example the word meaning "doctor" and pronounced /ɖɔk.ʈor/ can be written in several ways in Bengali: in Bengali script as ডক্টর or as the abbreviation ডঃ; and in Latin script as the English loanword doctor or as the abbreviation Dr. A TTS system should accept all ways of writing this word, hence all written variations are in our pronunciation lexicon.
A Bengali TTS voice should further be able to pronounce a variety of common brand names written in Latin script. The linguists from Project Unison therefore transcribed a few thousand such words phonemically into Bengali. For example, "WhatsApp" was transcribed /ho.aʈs.æp/, and "Google" was straightforwardly transcribed as /gu.gol/ just as if it had been spelled গুগল.
Overall our linguists transcribed more than 65,000 Bengali words into phonemic notation. In an effort to contribute to the community working on speech synthesis, speech recognition, and related natural language efforts, we are releasing our
Bengali pronunciation dictionary
under a
Creative Commons License (CC BY 4.0)
. It is our hope that this will be a valuable resource for researchers and developers who are improving the state of spoken language systems.
Despite our efforts, this Bengali dictionary is incomplete and contains residual errors. As a work-in-progress it will continue to improve over time. We are hoping that other natural language and speech researchers will join us in making available more datasets under open licenses. As we refine our development process and extend it to more languages, we are planning on releasing additional datasets for other languages in the future.
NEXT UP: One Down, 299 to Go (Ep 4)
Labels
2018
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
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
.
