During Interspeech 2011, the 12th annual Conference of the International Speech Communication Association being held in Florence, Italy, from Aug. 28 to 31, researchers from Microsoft Research will present work that dramatically improves the potential of real-time, speaker-independent, automatic speech recognition.

Dong Yu, researcher at Microsoft Research Redmond, and Frank Seide, senior researcher and research manager with Microsoft Research Asia, have been spearheading this work, and their teams have collaborated on what has developed into a research breakthrough in the use of artificial neural networks for large-vocabulary speech recognition.

The Holy Grail of Speech Recognition

Commercially available speech-recognition technology is behind applications such as voice-to-text software and automated phone services. Accuracy is paramount, and voice-to-text typically achieves this by having the user “train” the software during setup and by adapting more closely to the user’s speech patterns over time. Automated voice services that interact with multiple speakers do not allow for speaker training because they must be usable instantly by any user. To cope with the lower accuracy, they either handle only a small vocabulary or strongly restrict the words or patterns that users can say.

The ultimate goal of automatic speech recognition is to deliver out-of-the-box, speaker-independent speech-recognition services—a system that does not require user training to perform well for all users under all conditions.

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