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An important goal at Google is to make spoken access ubiquitously available. Achieving ubiquity requires two things: availability (i.e., built into every possible interaction where speech input or output can make sense) and performance (i.e., works so well that the modality adds no friction to the interaction).This chapter is a case study of the development of Google Search by Voice -a step toward our long-term vision of ubiquitous access. While the integration of speech input into Google search is a significant step toward more ubiquitous access, it has posed many problems in terms of the performance of core speech technologies and the design of effective user interfaces. Work is ongoing and no doubt the problems are far from solved. Nonetheless, we have at the minimum achieved a level of performance showing that usage of voice search is growing rapidly, and that many users do indeed become repeat users.

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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.

The paper presents an empirical exploration of google.com query stream language modeling. We describe the normalization of the typed query stream resulting in out-of-vocabulary (OoV) rates below 1% for a one million word vocabulary. We present a comprehensive set of experiments that guided the design decisions for a voice search service. In the process we re-discovered a less known interaction between Kneser-Ney smoothing and entropy pruning, and found empirical evidence that hints at non-stationarity of the query stream, as well as strong dependence on various English locales-USA, Britain and Australia.

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