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Speech Recognition for Smart Homes
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· July 2008
DOI: 10.5772/6363
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Ian Mcloughlin
Singapore Institute of Technology (SIT)
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Hamid Reza Sharifzadeh
UNITEC
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27
Speech Recognition for Smart Homes
Ian McLoughlin
and Hamid Reza Sharifzadeh
Nanyang Technological University
Singapore
1. Introduction
When Christopher Sholes created the QWERTY keyboard layout in the 1860s (often
assumed to be for slowing down fast typists), few would have imagined that his invention
would become the dominant input device of the 20th century. In the early years of the 21st
century (the so called 'speed and information' century), its use remains dominant, despite
many, arguably better, input devices having been invented. Surely it
is time to consider
alternatives, in particular the most natural method of human communications – spoken
language.
Spoken language is not only natural, but in many cases is faster than typed, or mouse-
driven input, and is accessible at times and in locations where keyboard, mouse and
monitor (KMM) may not be convenient to use. In particular,
in a world with growing
penetration of embedded computers, the so-called 'smart home' may well see the first mass-
market deployment of vocal interaction (VI) systems.
What is necessary in order to make VI a reality within the smart home? In fact much of the
underlying technology already exists – many home appliances, electrical devices,
infotainment systems, sensors and so on are sufficiently intelligent to be networked.
Wireless
home networks are fast, and very common. Speech synthesis technology can
generate natural sounding speech. Microphone and loudspeaker technology is well-
established. Modern computers are highly capable, relatively inexpensive, and – as
embedded systems – have already penetrated almost all parts of a modern home. However
the bottleneck in the realisation of smart home systems appears
to have been the automatic
speech recognition (ASR) and natural language understanding aspects.
In this chapter, we establish the case for automatic speech recognition (ASR) as part of VI
within the home. We then overview appropriate ASR technology to present an analysis of
the environment and operational conditions within the home related to ASR, in particular
the argument of restricting vocabulary size to improve recognition accuracy. Finally, the
discussion concludes with details on modifications to the widely used Sphinx ASR
system
for smart home deployment on embedded computers. We will demonstrate that such
deployments are sensible, possible, and in fact will be coming to homes soon.
