A model is presented which predicts the movements of flesh points on the tongue, lips, and jaw during speech production, from time-aligned phonetic strings. Starting from a database of x-ray articulator trajectories, means and variances of articulator positions and curvatures at the midpoints of phonemes are extracted from the data set. During prediction, the amount of articulatory effort required in a particular phonetic context is estimated from the relative local curvature of the articulator trajectory concerned. Correlations between position and curvature are used to directly predict variations from mean articulator positions due to coarticulatory effects. Use of the explicit coarticulation model yields a significant increase in articulatory modeling accuracy with respect to x-ray traces, as compared with the use of mean articulator positions alone.
We describe a self-organising pseudo-articulatory speech production model (SPM) trained on an X-ray microbeam database, and present results when using the SPM within a speech recognition framework. Given a time-aligned phonemic string, the system uses an explicit statistical model of co-articulation to generate pseudoarticulator trajectories. From these, parametrised speech vectors are synthesised using a set of artificial neural networks (ANNs). We present an analysis of the articulatory information in the database used, and demonstrate the improvements in articulatory modelling accuracy obtained using our co-articulation system. Finally, we give results when using the SPM to re-score N-best utterance transcription lists as produced by the CUED HTK Hidden Markov Model (HMM) speech recognition system. Relative reductions of 18% in the phoneme error rate and 15% in the word error rate are achieved.
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