Details of Grant 

EPSRC Reference:	GR/R67224/01
Title:	Automatically-determined Unit Inventories for Automatic Speech Recognition
Principal Investigator:	King, Professor S
Other Investigators:
Researcher Co-Investigators:
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Department:	Centre for Speech Technology Research
Organisation:	University of Edinburgh
Scheme:	Fast Stream
Starts:	01 January 2003	Ends:	30 June 2006	Value (£):	63,684
EPSRC Research Topic Classifications:	Human Communication in ICT
EPSRC Industrial Sector Classifications:
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Panel History:
Summary on Grant Application Form
Conventional automatic speech recognition (ASR) systems generally model speech as a sequence of phones, using Hidden Markov Models (HMMs) of those phones. The acoustic realisation of phonemes depends heavily on surrounding context, so context-dependent models are used. Phonemes are a useful linguistic device, but are they the best unit for ASR?An alternative view of speech is as a set of parallel feature streams, such as phonetic features (vowel height, frication, voicing, etc.). Contextual effects are more simply described in terms of such features. Phonetic features (or indeed direct articulatory measurements) do not change value synchronously at phone boundaries. However these effects are not easily described, or accounted for, using conventional phone-sized units, and conventional HMMs cannot generate asynchronous observation streams.Two things are required: an acoustic model which can account for asynchrony and an inventory of appropriate units to model. In this project we build on previous work which investigated the first requirement, and now address the second. The project will investigate methods for optimising the inventory of units to be modelled in conjunction with alternative acoustic models to the HMM. This will allow such models to realise their true potential, which until now has been restricted by the use of unit inventories best suited to the HMM (e.g. triphones).
Key Findings
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Organisation Website:	http://www.ed.ac.uk