Limited Training Data Robust Speech Recognition Using Kernel-Based Acoustic Models

Conference PaperinAcoustics, Speech, and Signal Processing, 1988. ICASSP-88., 1988 International Conference on 1:I - I · June 2006with11 Reads
Impact Factor: 4.63 · DOI: 10.1109/ICASSP.2006.1660226 · Source: IEEE Xplore
Conference: Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on, Volume: 1

    Abstract

    Contemporary automatic speech recognition uses hidden-Markov-models (HMMs) to model the temporal structure of speech where one HMM is used for each phonetic unit. The states of the HMMs are associated with state-conditional probability density functions (PDFs) which are typically realized using mixtures of Gaussian PDFs (GMMs). Training of GMMs is error-prone especially if training data size is limited. This paper evaluates two new methods of modeling state-conditional PDFs using probabilistically interpreted support vector machines and kernel Fisher discriminants. Extensive experiments on the RMI (P. Price et al., 1988) corpus yield substantially improved recognition rates compared to traditional GMMs. Due to their generalization ability, our new methods reduce the word error rate by up to 13% using the complete training set and up to 33% when the training set size is reduced