Blog
The latest news from Google AI
Assessing the Quality of Long-Form Synthesized Speech
Monday, September 9, 2019
Posted by Tom Kenter, Google Research, London
Automatically generated speech is everywhere, from directions being read out aloud while you are driving, to virtual
assistants
on your phone or
smart speaker devices
at home. While much research is being done to try to make synthesized speech sound as natural as possible—such as
generating speech for low-resource languages
and
creating human-like speech with Tacotron 2
—how does one evaluate the generated speech? The best way to find out is to ask people, who are very good at telling if something sounds natural or not.
In the field of speech synthesis, subjects are routinely asked to listen to samples of synthesized speech and rate their quality. Yet, until now, evaluation of synthesized speech has been done on a sentence-by-sentence basis. But often one wants to know the quality of a series of sentences that belong together, such as a paragraph in a news article or a turn in a conversation. This is where it gets interesting, as there is more than one way of evaluating sentences that naturally occur in a sequence, and, surprisingly, a rigorous comparison of these different methods has not been carried out. This in turn can hinder research progress in developing products that rely on generated speech.
To address this challenge, we present “
Evaluating Long-form Text-to-Speech: Comparing the Ratings of Sentences and Paragraphs
”, a publication to appear at
SSW10
in which we compare several ways of evaluating synthesized speech for multi-line texts. We find that when a sentence is evaluated as part of a longer text involving several sentences, the outcome is influenced by the way in which the audio sample is presented to the people evaluating it. For example, when the sentence is presented by itself, without any context, the rating people give on average is substantially different from the rating they give when they listen to
the same sentence
with some context (while the context doesn't have to be rated).
Evaluating Automatically Generated Speech
To determine the quality of speech signals, it is common practice to ask several human raters to give their opinion for a particular sample, on a 1-to-5 scale. This sample can be automatically generated, but it can also be natural speech (i.e., an actual person saying a sentence out loud), which serves as a control. The scores of all reviewers rating a particular speech sample are averaged to get a Mean Opinion Score (MOS).
Until now, MOS ratings were typically collected per sentence, i.e., raters listened to sentences in isolation to form their opinion. Instead of this typical approach, we consider three different ways of presenting speech samples to raters—both with and without context—and we show that each approach yields different results. The first,
presenting the sentence in isolation
, is the default method commonly used in the field. An alternative method is to
provide the full context for the sentence
. In this case, the entire paragraph to which the sentence belongs is included and the ensemble is rated. The final approach is to
provide a context-stimulus pair
. Here, rather than providing full context, only some context is provided, such as the preceding sentence(s) from the original paragraph.
Interestingly, these three different approaches for presenting speech give different results
even when applied to natural speech
. This is demonstrated in the figure below, where the MOS scores are presented for natural speech samples rated using the three different methods of presentation. Even though the sentences being rated are identical across the three different settings, the scores are different on average, depending on the context in which they were presented.
MOS results for natural speech from a dataset consisting of news articles. Though the differences appear small, they are significant between all conditions (two-tailed
t-test
with α=0.05).
Examination of the figure above reveals that raters rarely give top scores (a five) even to recorded human speech, which may be surprising. However, this is a typical result seen in sentence evaluation studies and probably has to do with a more generic pattern of behavior, that people tend to avoid using the extreme ends of a scale, regardless of the task or setting.
When evaluated synthesized speech, the differences are more pronounced.
MOS results for synthesized speech on the same news article dataset used above. All lines are synthesized speech, unless indicated otherwise.
To see if the way context is presented makes a difference, we tried several different ways of providing it: one or two sentences leading up to the sentence to be evaluated, provided as generated speech or real speech. When context is added, the scores get higher (the four blue bars on the left) except when the context presented is real speech, in which case the score drops (the rightmost blue bar). Our hypothesis is that this has to do with an anchoring effect—if the context is very good (real speech) the synthesized speech, in comparison, is perceived as less natural.
Predicting Paragraph Score
When an entire paragraph of synthesized speech is played (the yellow bar), this is perceived as even less natural than in the other settings. Our original hypothesis was a weakest-link argument—the rating is probably as bad as the worst sentence in the paragraph. If that were the case, it should be easy to predict the rating of a paragraph by considering the ratings of the individual sentences in it, perhaps simply taking the minimum value to get the paragraph rating. It turns out, however, that does not work.
The failure of the weakest-link hypothesis may be due to more subtle factors that are difficult to tease out with such a simple approach. To test this, we also trained a machine learning algorithm to predict the paragraph score from the individual sentences. However, this approach, too, was unable to successfully predict paragraph scores reliably.
Conclusion
Evaluating synthesized speech is not straightforward when multiple sentences are involved. The traditional paradigm of rating sentences in isolation does not give the full picture, and one should be aware of anchoring effects when context is provided. Rating full paragraphs might be the most conservative approach. We hope our findings help advance future work in speech synthesis where long-form content is concerned, such as audio book readers and conversational agents.
Acknowledgments
Many thanks to all authors of the paper: Rob Clark, Hanna Silen, Ralph Leith.
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
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
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
.
