@phdthesis{MariHilario2004, author = {Mar{\´i} Hilario, Joan}, title = {Discriminative connectionist approaches for automatic speech recognition in cars}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-000000649}, school = {BTU Cottbus - Senftenberg}, year = {2004}, abstract = {The first part of this thesis is devoted to the evaluation of approaches which exploit the inherent redundancy of the speech signal to improve the noise robustness. On the basis of this evaluation on the AURORA 2000 database, we further study in detail two of the evaluated approaches. The first of these approaches is the hybrid RBF/HMM approach, which is an attempt to combine the superior classification performance of radial basis functions (RBFs) with the ability of HMMs to model time variation. The second approach is using neural networks to non-linearly reduce the dimensionality of large feature vectors including context frames. We propose the use of different MLP topologies for that purpose. Experiments on the AURORA 2000 database reveal that the performance of the first approach is similar to the performance of systems based on SCHMMs. The second approach cannot outperform the performance of linear discriminant analysis (LDA) on a database recorded in real car environments, but it is on average significantly better than LDA on the AURORA 2000 database.}, subject = {Kraftfahrzeuginnenraum; Automatische Spracherkennung; Sprachqualit{\"a}t; Sprachsignal; St{\"o}rsignal; Redundanzanalyse; Neuronales Netz; Speech recognition; Noise robustness; Evaluation; AURORA; Connectionist}, language = {en} }