Making an Impact on a Thriving Speech Research Community

Posted by Vincent Vanhoucke, Google Research

While we continue to launch exciting new speech products--most recently Voice Actions and Google Search by Voice in Russian, Czech and Polish--we also strive to contribute to the academic research community by sharing both innovative techniques and experiences with large-scale systems.

This year’s gathering of the world’s experts in speech technology research, Interspeech 2010 in Makuhari, Japan, which Google co-sponsored, was a fantastic demonstration of the momentum of this community, driven by new challenges such as mobile voice communication, voice search, and the increasing international reach of speech technologies.

Googlers published papers that showcased the breadth and depth of our speech recognition research. Our work addresses both fundamental problems in acoustic and language modeling, as well as the practical issues of building scalable speech interfaces that real people use everyday to make their lives easier.

Here is a list of the papers presented by Googlers at the conference:

• Direct Construction of Compact Context-Dependency Transducers From Data, David Rybach and Michael Riley (Computer Speech & Language Best Paper Award).
• Voice Search for Development, Etienne Barnard, Johan Schalkwyk, Charl van Heerden and Pedro J. Moreno.
• Unsupervised Discovery and Training of Maximally Dissimilar Cluster Models, Françoise Beaufays, Vincent Vanhoucke and Brian Strope.
• Search by Voice in Mandarin Chinese, Jiulong Shan, Genqing Wu, Zhihong Hu, Xiliu Tang, Martin Jansche and Pedro J. Moreno.
• On-Demand Language Model Interpolation for Mobile Speech Input, Brandon Ballinger, Cyril Allauzen, Alexander Gruenstein, and Johan Schalkwyk.
• Building Transcribed Speech Corpora Quickly and Cheaply for Many Languages, Thad Hughes, Kaisuke Nakajima, Linne Ha, Atul Vasu, Pedro J. Moreno and Mike LeBeau.
• Say What? Why Users Choose to Speak their Web Queries, Maryam Kamvar and Doug Beeferman.
• Study on Interaction between Entropy Pruning and Kneser-Ney Smoothing, Ciprian Chelba, Thorsten Brants, Will Neveitt and Peng Xu.
• Decision Tree State Clustering with Word and Syllable Features, Hank Liao, Chris Alberti, Michiel Bacchiani and Olivier Siohan.