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Wireless sensor networks promise an unprecedented opportunity to monitor physical environments via inexpensive wireless embedded devices. Given the sheer amount of sensed data, efficient classification of them becomes a critical task in many sensor network applications. The large scale and the stringent energy constraints of such networks however challenge(More)
Cross-lingual voice transformation is challenging when source language (L1) and target language (L2) are very different in corresponding phonetics and prosodies. We propose a frame mapping based HMM approach to this problem. The source speaker's speech data is first warped in frequency toward the target speaker by mapping corresponding formants of selected(More)
Recent work demonstrates impressive success of the bottleneck (BN) feature in speech recognition, particularly with deep networks plus appropriate pre-training. A widely admitted advantage associated with the BN feature is that the network structure can learn multiple environmental conditions with abundant training data. For tasks with limited training(More)
Expectation Maximization (EM) is among the most popular algorithms for estimating parameters of statistical models. However, EM, which is an iterative algorithm based on the maximum likelihood principle, is generally only guaranteed to find stationary points of the likelihood objective, and these points may be far from any maximizer. This article addresses(More)