Gradient-domain processing is widely used to edit and combine images. In this article we extend the framework in two directions. First, we adapt the gradient-domain approach to operate on a spherical domain, to enable operations such as seamless stitching, dynamic-range compression, and gradient-based sharpening over spherical imagery. An efficient streaming computation is obtained using a new spherical parameterization with bounded distortion and localized boundary constraints. Second, we design a distributed solver to efficiently process large planar or spherical images. The solver partitions images into bands, streams through these bands in parallel within a networked cluster, and schedules computation to hide the necessary synchronization latency. We demonstrate our contributions on several datasets including the Digitized Sky Survey, a terapixel spherical scan of the night sky.

We investigate several measures of voice quality (VQ) to improve tone recognition in Mandarin Chinese. We find that band energy measures such as spectral balance (Sluijter and van Heuven, 1996) work better than measures based on glottal flow estimation and harmonic-formant differences. We also determine a set of bands and measures that improve tone classification accuracy on broadcast news speech to 64.1% from 60.4% when added to a traditional pitch-duration-intensity set of features. Most improvement is for the neutral tone, for which the F score increases from 0.345 to 0.619.

We use a combination of linear support vector machines and hidden markov models for dialog act tagging in the HCRC MapTask corpus, and obtain better results than those previously reported. Support vector machines allow easy integration of sparse highdimensional text features and dense low-dimensional acoustic features, and produce posterior probabilities usable by sequence labelling algorithms. The relative contribution of text and acoustic features for each class of dialog act is analyzed. 1.

Native speakers of Mandarin produce and perceive tones in ways that depend on the focus with which each word is produced [1]. This paper gives one approach to improving tone recognition algorithms using focus, by training different support vector machines on syllables conditional on their position with respect to the focused word in a sentence. In a four-way tone classification task on focus-labelled laboratory Mandarin speech data collected by Xu [2], error rates improve from 15.2% without using focus to 8.7% when using focus. Using the fact that tones on syllables in focused words are especially easy to recognize, we propose a tone recognition algorithm that makes use of focus without requiring focus labels in either training or test set. The algorithm has an error rate of 9.8% on this data set.