Discriminative joint modeling of acoustic and lexical variations for spoken language processing

Speech recognition systems consist of three components, namely, the acoustic model, the pronunciation model and the language model. The acoustic and language models are typically learned separately and furthermore optimized for different cost functions. This framework has been a result of historical and practical considerations such as the availabil- ity of limited amounts of training data and the computational cost. These considerations are currently being overcome. Arguably, learning both models jointly to directly minimize the word error rate will result in a better recognizer. One of the contributions of this thesis is a detailed investigation of a discriminative framework to jointly learn the parameters of the acoustic, language and duration models (commonly captured with the parameters from the acoustic models).