Articulatory feature detection with Support Vector Machines for integration into ASR and phone recognition

We study the use of support vector machines (SVM) for detecting the occurrence of articulatory features in speech audio data and using the information contained in the detector outputs to improve phone and speech recognition. Our expectation is that an SVM should be able to appropriately model the separation of the classes which may have complex distributions in feature space. We show that performance improves markedly when using discriminatively trained speaker dependent parameters for the SVM inputs, and compares quite well to results in the literature using other classifiers, namely artificial neural networks (ANN). Further, we show that the resulting detector outputs can be successfully integrated into a state of the art speech recognition system, with consequent performance gains. Notably, we test our system on English broadcast news data from dev04f.