Support vector machine fusion of idiolectal and acoustic speaker information in Spanish conversational speech

This paper proposes a support vector machine (SVM) based combining scheme that incorporates ideolectal and acoustic characteristics for speaker recognition. Two statistical model paradigms, namely GMM for acoustic modeling and bigrams for language modeling, provide multilevel speaker information that affords a better classification performance when SVM-based fusion is accomplished. This combining approach is useful for all speaker recognition tasks where a considerable amount of data is available. Motivated by the absence of Spanish databases that made feasible our research experiments, more than nine hours of Spanish conversational speech was collected and manually transcribed from broadcasted radio talk shows.