Publications
A hybrid SVM/MCE training approach for vector space topic identification of spoken audio recordings
Summary
Summary
The success of support vector machines (SVMs) for classification problems is often dependent on an appropriate normalization of the input feature space. This is particularly true in topic identification, where the relative contribution of the common but uninformative function words can overpower the contribution of the rare but informative content...
Language recognition with discriminative keyword selection
Summary
Summary
One commonly used approach for language recognition is to convert the input speech into a sequence of tokens such as words or phones and then to use these token sequences to determine the target language. The language classification is typically performed by extracting N-gram statistics from the token sequences and...
Topic identification from audio recordings using word and phone recognition lattices
Summary
Summary
In this paper, we investigate the problem of topic identification from audio documents using features extracted from speech recognition lattices. We are particularly interested in the difficult case where the training material is minimally annotated with only topic labels. Under this scenario, the lexical knowledge that is useful for topic...
Language recognition with word lattices and support vector machines
Summary
Summary
Language recognition is typically performed with methods that exploit phonotactics--a phone recognition language modeling (PRLM) system. A PRLM system converts speech to a lattice of phones and then scores a language model. A standard extension to this scheme is to use multiple parallel phone recognizers (PPRLM). In this paper, we...