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Measurement of peak vertical ground reaction force (GRFz) from multiple limbs simultaneously during high-speed, over-ground locomotion would enhance our understanding of the locomotor mechanics of cursorial animals. Here, we evaluate the accuracy of predicting peak GRFz from duty factor (the proportion of the stride for which the limb is in contact with the(More)
For many applications, it is necessary to produce speech transcriptions in a causal fashion. To produce high quality transcripts , speaker adaptation is often used. This requires online speaker clustering and incremental adaptation techniques to be developed. This paper presents an integrated approach to online speaker clustering and adaptation which allows(More)
An increasingly common scenario in building speech synthesis and recognition systems is training on inhomogeneous data. This paper proposes a new framework for estimating hidden Markov models on data containing both multiple speakers and multiple languages. The proposed framework, speaker and language factorization, attempts to factorize(More)
Spoken content in languages of emerging importance needs to be searchable to provide access to the underlying information. In this paper, we investigate the problem of extending data fusion methodologies from Information Retrieval for Spoken Term Detection on low-resource languages in the framework of the IARPA Babel program. We describe a number of(More)
Rapidly adapting a speech recognition system to new speakers using a small amount of adaptation data is important to improve initial user experience. In this paper, a count-smoothing framework for incorporating prior information is extended to allow for the use of different forms of dynamic prior and improve the robustness of transform estimation on small(More)
This paper investigates the use of Gaussian Selection (GS) to increase the speed of a large vocabulary speech recognition system. Typically 30-70% of the computational time of a HMM-based speech recogniser is spent calculating probabilities. The aim of GS is to reduce this load by dividing the acoustic space into a set of clusters and associating a(More)
Current text-to-speech synthesis (TTS) systems are often perceived as lacking expressiveness, limiting the ability to fully convey information. This paper describes initial investigations into improving expressiveness for statistical speech synthesis systems. Rather than using hand-crafted definitions of expressive classes, an unsupervised clustering(More)
This paper investigates the use of Gaussian Selection (GS) to reduce the state likelihood computation in HMM-based systems. These likelihood calculations contribute significantly (30 to 70%) to the computational load. Previously, it has been reported that when GS is used on large systems the recognition accuracy tends to degrade above a 3 reduction in(More)