The most exciting work I’ve been involved with on the Sound Understanding team has been the development of Tacotron, an end-to-end speech synthesis system that produces speech “end-to-end” from characters to waveform. The initial system took verbalized characters as input and produced a log-magnitude mel spectrogram, which we then synthesize to waveform via standard signal processing methods (Griffin-Lim and an inverse Short-time Fourier Transform). In Tacotron 2, we replaced this hand-designed synthesis method with a neural vocoder, initially based on WaveNet.

One line of research on my team is direct-to-waveform acoustic models, skipping the intermediate spectrogram representation. In the Wave-Tacotron paper, we published a model that does just that.

Check out our publications page for the full list of research that my team has co-authored with the Google Brain, Deepmind, and Google Speech teams.

In 2015, I joined the Sound Understanding team within Google Perception. We focus on building systems that can both analyze and synthesize sound. Being able to work on my hobby (sound and digital signal processing) as my full time job has been a dream come true. We operate as a hybrid research team, which means we both publish our work and deploy it to improve Alphabet’s products and services.

I’ve had the opportunity to work on some neat tasks and projects during my time on the team, but speech synthesis has been what I’ve spent the most time working on.

In 2015, I joined the Sound Understanding team within Google Perception. Our main tool for machine learning research is TensorFlow. Over the years I’ve contributed a number of features to TensorFlow that have been crucial to the research work my team has done.

The highlights include:

• Added the tf.signal module of signal processing components.
• Extended real and complex FFT support, implemented with Eigen (CPU), cuFFT (GPU), and TPU support.
• Significantly expanded complex number support.
• Bugfixes and contributions to various parts of the runtime, libraries and more.

Check out the full list of commits on GitHub.

As part of my work on the Google Knowledge Graph, I was super happy to play a small role in the creation of Inbox, Google’s fresh take on email. I worked closely with the Inbox team to bring some beautiful imagery and Knowledge Graph smarts to Inbox’s smart email annotations. The beautiful photos you see in Inbox are partly the result of my work.

In June 2015, we launched Trip bundles in Inbox, showing a summary of your upcoming and previous trips.

Another public Google project that I have played a significant part in is the Google Knowledge Graph.

There are many benefits to indexing human knowledge in structured form (as opposed to indexing strings for keyword search). The features we are adding to search that make use of this data are reason enough to develop the Knowledge Graph because they are very useful to searchers today. However, I think we are only at 1 percent of what is possible with having a structured knowledge repository and that is the exciting part to me.

Since 2011 I have been working at an obscure web startup called Google. I’ve been working on various search projects, some of which have seen the light of day.

One project I am particularly proud of is called symptom search. A lot of people use Google to search for their symptoms. Sometimes, it may even be time-critical or life threatening (such as a query for [chest pain]).

To help users better understand the medical conditions associated with their search terms, symptom search shows you a list of conditions most relevant to your search terms when you seem to be searching for information about medical symptoms.

We launched this is February 2012 and it has been helping many users find information about their symptoms.