Philip C. Woodland

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A method of speaker adaptation for continuous density hidden Markov models (HMMs) is presented. An initial speaker-independent system is adapted to improve the modelling of a new speaker by updating the HMM parameters. Statistics are gathered from the available adaptation data and used to calculate a linear regressionbased transformation for the mean(More)
In this paper we introduce the Minimum Phone Error (MPE) and Minimum Word Error (MWE) criteria for the discriminative training of HMM systems. The MPE/MWE criteria are smoothed approximations to the phone or word error rate respectively. We also discuss I-smoothing which is a novel technique for smoothing discriminative training criteria using statistics(More)
One of the key issues for adaptation algorithms is to modify a large number of parameters with only a small amount of adaptation data. Speaker adaptation techniques try to obtain near speaker dependent (SD) performance with only small amounts of speaker speci c data, and are often based on initial speaker independent (SI) recognition systems. Some of these(More)
This paper describes a framework for optimising the structure and parameters of a continuous density HMM-based large Ž . vocabulary recognition system using the Maximum Mutual Information Estimation MMIE criterion. To reduce the computational complexity of the MMIE training algorithm, confusable segments of speech are identified and stored as word lattices(More)
This paper describes, and evaluates on a large scale, the lattice based framework for discriminative training of large vocabulary speech recognition systems based on Gaussian mixture hidden Markov models (HMMs). The paper concentrates on the maximum mutual information estimation (MMIE) criterion which has been used to train HMM systems for conversational(More)
This paper presents a simple and robust consensus decoding approach for combining multiple machine translation (MT) system outputs. A consensus network is constructed from an N-best list by aligning the hypotheses against an alignment reference, where the alignment is based on minimising the translation edit rate (TER). The minimum Bayes risk (MBR) decoding(More)