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Andreas Spanias
Researcher at Arizona State University
Publications - 512
Citations - 8918
Andreas Spanias is an academic researcher from Arizona State University. The author has contributed to research in topics: Speech coding & Speech processing. The author has an hindex of 36, co-authored 490 publications receiving 7895 citations. Previous affiliations of Andreas Spanias include Arizona's Public Universities & Intel.
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Proceedings ArticleDOI
A multi-modal approach to emotion recognition using undirected topic models
TL;DR: A multi-modal framework for emotion recognition using bag-of-words features and undirected, replicated softmax topic models is proposed here, showing that a turn of 1 second duration can be classified in approximately 666.65ms, thus making this method highly amenable for real-time implementation.
Journal ArticleDOI
Robust Consensus in the Presence of Impulsive Channel Noise
TL;DR: This work is the first of its kind in the literature to propose a consensus algorithm which relaxes the requirement of finite moments on the communication noise and it is shown that the nodes reach consensus asymptotically to a finite random variable.
Proceedings ArticleDOI
On-line laboratory for communication systems using J-DSP
TL;DR: Assessment results indicate that the majority of the students responded that the new JDSP functionality and the associated lab exercises complemented well the theory covered in class and helped them develop intuition on the communications concepts covered in these labs.
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Within and cross-corpus speech emotion recognition using latent topic model-based features
TL;DR: A supervised replicated softmax model (sRSM), based on restricted Boltzmann machines and distributed representations, is proposed to learn naturally discriminative topics and is evaluated for the recognition of categorical or continuous emotional attributes via within and cross-corpus experiments.
Proceedings ArticleDOI
Supervised local sparse coding of sub-image features for image retrieval
TL;DR: This paper develops a feature extraction method that uses multiple global/local features extracted from large overlapping regions of an image, which they refer to as sub-images, and proposes a procedure for dictionary design and supervised local sparse coding of sub-image heterogeneous features.