Deploying GOOG-411: Early lessons in data, measurement, and testing

Abstract

We describe our early experience building and optimizing GOOG-411, a fully automated, voice-enabled, business finder. We show how taking an iterative approach to system development allows us to optimize the various components of the system, thereby progressively improving user-facing metrics. We show the contributions of different data sources to recognition accuracy. For business listing language models, we see a nearly linear performance increase with the logarithm of the amount of training data. To date, we have improved our correct accept rate by 25% absolute, and increased our transfer rate by 35% absolute.