K
Kenneth John Basye
Researcher at Amazon.com
Publications - 9
Citations - 432
Kenneth John Basye is an academic researcher from Amazon.com. The author has contributed to research in topics: Speech processing & Audio signal. The author has an hindex of 7, co-authored 9 publications receiving 432 citations.
Papers
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Patent
Speech recognition power management
Kenneth John Basye,Hugh Evan Secker-Walker,Tony David,Reinhard Kneser,Jeffrey Penrod Adams,Stan Weidner Salvador,Mahesh Krishnamoorthy +6 more
TL;DR: In this article, power consumption for a computing device may be managed by one or more keywords, such as a keyword, network interface module and/or application processing module of the computing device.
Patent
Audible command filtering
TL;DR: In this paper, audio fingerprinting and speech recognition techniques are used to determine whether the wake word and/or command substantially matches the audio of a known television or radio advertisement, program, broadcast, etc.
Patent
Keyword detection modeling using contextual and environmental information
Rohit Prasad,Kenneth John Basye,Spyridon Matsoukas,Rajiv Ramachandran,Shiv Naga Prasad Vitaladevuni,Bjorn Hoffmeister +5 more
TL;DR: In this paper, features are disclosed for detecting words in audio using environmental information and/or contextual information in addition to acoustic features associated with the words to be detected, and a detection model can be generated and used to determine whether a particular word such as a keyword or "wake word" has been uttered.
Patent
Input speech quality matching
TL;DR: In this paper, a system uses trained models to detect a speech quality and generate an indicator of the speech quality, which is sent to downstream components of the system such as a command processor or TTS system.
Patent
Methods and systems for obtaining language models for transcribing communications
TL;DR: A method for transcribing a spoken communication includes acts of receiving a spoken first communication from a first sender to a first recipient, obtaining information relating to a second communication, which is different from the first communication, from a second sender to another recipient, using the obtained information to obtain a language model, and using the language model to transcribe the spoken first communications as mentioned in this paper.