“Your Word is my Command”: Google Search by Voice: A Case Study

By Johan Schalkwyk, Doug Beeferman, Franccoise Beaufays, Bill Byrne, Ciprian Chelba, Michael M. Cohen, Maryam Kamvar, Brian Strope. 2010

253 citations recorded by the source corpus.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jinyu Li
2. Tara N. Sainath
3. Dong Yu
4. Bo Li
5. Rohit Prabhavalkar
6. Yonghui Wu
7. Quoc V. Le
8. Yu Zhang

Citations in the corpus

• Streaming End-to-end Speech Recognition for Mobile Devices (2018, background): “Coupled with the tremendous growth and adoption of smartphones, tablets and other consumer devices, these improvements have resulted in speech becoming one of the primary modes of interaction with such devices [2, 3].”
• Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces (2018, background): “This achievement unlocked many practical applications of voice assistants which are now used in many fields from customer support [6, 47], to autonomous cars [41], or smart homes [16, 26].”
• Small-footprint keyword spotting using deep neural networks (2014, background): “For example, Google offers the ability to search by voice [1] on Android devices and Apple’s iOS devices are equipped with a conversational assistant named Siri.”
• Convolutional neural networks for small-footprint keyword spotting (2015, background): “For example, Google offers the ability to search by voice [1] on Android phones, while personal assistants such as Google Now, Apple’s Siri, Microsoft’s Cortana and Amazon’s Alexa, all utilize speech recognition to interact with these systems.”
• Automatic gain control and multi-style training for robust small-footprint keyword spotting with deep neural networks (2015, background): “Services such as Google voice search [1] and conversational assistants such as Google Now, Apple’s Siri and Microsoft’s Cortana prominently feature speech technologies as a means of interacting with and controlling devices.”
• Multilingual acoustic models using distributed deep neural networks (2013, background): “Speech recognition has advanced remarkably over the last decade and is used in a growing number of applications and services such as Google Voice Search [1].”
• Multi-modal integration of dynamic audiovisual patterns for an interactive reinforcement learning scenario (2016, methodology): “Afterwards they become the input of the multi-modal integrative system implemented by an associative neural architecture to obtain the integrated advice label λ I and the integrated confidence value γ I . is based on Google Voice Search (GVS) [17], a cloud-based ASR service to process audio data cap”
• Speech acoustic modeling from raw multichannel waveforms (2015, methodology): “We train and evaluate the proposed network on several large-vocabulary Voice Search [17] datasets.”
• On the compression of recurrent neural networks with an application to LVCSR acoustic modeling for embedded speech recognition (2016, background): “As speech recognition technologies continue to improve, they are becoming increasingly ubiquitous on mobile devices: voice assistants such as Apple’s Siri, Microsoft’s Cortana, Amazon’s Alexa and Google Now [6] enable users to search for information using their voice.”
• Survey on Deep Neural Networks in Speech and Vision Systems (2019, background): “Google provides ASR-based search services [107] on Android, iOS, and Chrome platforms, and Apple iOS devices, which are equipped with conversational assistant named Siri.”