9408896 Ostendorf This is a standard award made as an extension to the research conducted under IRI-8902124 and is funded under ARPA's program competition for the Augmentation Awards for Science and Engineering Research Training. The research proposed investigates model-based approaches or the representation of channel noise and distortion via cepstrum and parametric transformations, involving one graduate and one undergraduate student following research topic stimulated by the previous award. Graduate student research under this award considers a segmental model approach to model-based channel compensation using parallel HMMs and maximum likelihood channel identification of both additive and convolutional noise. The undergraduate research topic involves the evaluation of signal processing approaches for noise and channel compensation as they compare with more classical approaches that use standard Melwarped cepstra and derivative features. Both of these projects complement the objective of the original award and augment it.
