A hybrid SVM/HMM acoustic modeling approach to automatic speech recognition

Acoustic models based on a NN/HMM framework have been used successfully on various recognition tasks for continuous speech recognition. Recently tied-posteriors have been introduced within this context. Here, we present an approach combining SVMs and HMMs using the tied-posteriors idea. One set of SVMs calculates class posterior probabilities and shares these probabilities among all HMMs. The number of SVMs is varied as well as the input context and the amount of training data. Applying a first implementation, results on the AURORA2 task show already a promising improvement of the word error rate compared to the baseline acoustic models.