Communications of the ACM

on data such as radio broadcasts and newspapers to build a credible system, King says, without the expense of hiring linguistic experts and professional voice artists. He has already built a Swahili prototype, which he says works pretty well.

King also has developed a system can take a small number of recordings of a particular individual’s speech and apply them to a model already trained with a much larger dataset, and use that to generate new speech that sounds like that individual. The system is undergoing clinical trials in a U.K. hospital to see if it can be a practical way of helping people with amyotrophic lateral sclerosis, who are expected to lose their ability to speak as their disease progresses. “This is not going to help them live any longer, but for the time they do live it could help make their quality of life better,” he says.

Further Reading

Van den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., and Kalchbrenner, N.

WaveNet: A Generative Model for Raw Audio, ArXiv, Cornell University Library, 2016 http://arxiv.org/pdf/1609.03499

King, S., and Karaiskos, V.

The Blizzard Challenge 2016, Blizzard Challenge Workshop, Sept. 2016, Cupertino, CA http://www.festvox.org/blizzard/bc2016/ blizzard2016_overview_paper.pdf

Arnela, M., Dabbaghchian, S., Blandin, R., Guasch, O., Engwall, O., Van Hirtum, A., and Pelorson, X.

Influence of vocal tract geometry simplifications on the numerical simulation of vowel sounds, Journal of the Acoustical Society of America, 140, 2016 http://dx.doi.org/10.1121/1.4962488

Deng, L., Li, J., Huang, J-T., Yao, K., Yu, D., Seide, F., Seltzer, M., Zweig, G., He, X., Williams, J., Gong, Y, and Acero, A.

Recent advances in deep learning for speech research at Microsoft, IEEE International Conference on Acoustics, Speech and Signal Processing, 2013 http://ieeexplore.ieee.org/xpls/abs_all. jsp?arnumber=6639345

Simon King – Using Speech Synthesis to Give Everyone Their Own Voice https://www.youtube.com/watch?v=xzL-pxcpo-E

Neil Savage is a science and technology writer based in Lowell, MA.

synthesis. At the moment, though, it takes several hours of computing to produce one second of speech, so it is not immediately practical.

A Physical Model

Oriol Guasch, a physicist and mathematician at Ramon Llull University in Barcelona, Spain, is also taking a computationally intensive approach to speech synthesis. He is working on mathematically modeling the entire human vocal tract. “We’d like to simulate the whole physical process, which will, in the end, generate the final sound,” he says.

To do that, he takes an MRI image of a person’s vocal tract as he is pronouncing, say, the vowel “E.” He then represents that geometry of the vocal folds, soft palate, lips, nose, and other parts with differential equations. Using that, he generates a computational mesh, a many-sided grid that approximates the geometry. The process is not easy; a desktop computer can generate a mesh with three to four million elements in about three or four hours to represent the short “A” sound, he says. A sibilant “S,” though, requires a computer with 1,000 processors to run for a week to generate 45 million elements. The added complexity of that sound arises from the air flowing between the teeth and creating turbulent eddies swirling in complex patterns. Imagine, then, the time required to produce a whole word, let alone a sentence.

Guasch sees his approach more as
an interesting computing challenge
than a practical attempt to create
speech. “The final goal is not just syn-
thesizing speech, it’s about reproduc-
ing the way the human body behaves,”
he says. “I believe when you have a
computational problem, it’s good to
face it from many different angles.”
The University of Edinburgh’s
King, on the other hand, is working
toward practical applications. He re-
cently received funding for a three-
year project, in conjunction with the
BBC World Service, to create text-to-
speech systems for languages that do
not have enough speakers to make de-
veloping a system a financially attrac-
tive process for companies. It should
be possible to use machine learning

ACM
Member
News

TRYING TO DETERMINE WHAT IS COMPUTABLE

Santosh
Vempala,
Distinguished
Professor of
Computer
Science in the
College of
Computing at the Georgia
Institute of Technology (Georgia
Tech), earned his undergraduate
degree in computer science at
the Indian Institute of
Technology, New Delhi, India,
and his Ph.D. in Algorithms,
Combinatorics, and
Optimization from Carnegie
Mellon University in 1997. “What
I really wanted to study was
theory of computation; what is
computable and what is not, with
what amount of resources.”

After obtaining his doctorate, Vempala became a professor of mathematics at the Massachusetts Institute of Technology, a post he held for almost 10 years before moving to Georgia Tech in 2006. There, Vempala served as the first director (from 2006 to 2011) of the Algorithms and Randomness Center, a think tank dedicated to exploring the theory of computing and optimization.

His research focuses on the intersection of algorithms, randomness, and geometry. “The relationship between algorithms and geometry has been mutually beneficial,” Vempala explains. Initially it was about using techniques mathematicians had developed to work with algorithms in new ways, but now the questions and answers have made a deep contribution to the development of algorithmic geometry, he says.

Vempala continues to be fascinated by whether certain problems have efficient solutions. Some of his recent research has been focused on trying to understand how the brain works, and the modeling of its computational abilities.

In 2008, Vempala launched an initiative called Computing for Good, which develops deployable computing solutions for social problems like inequality, homelessness, and healthcare delivery, in areas where resources are constrained.