Abstract
We describe a novel approach to modeling idiosyncratic prosodic behavior for automatic speaker recognition. The approach computes various duration, pitch, and energy features for each estimated syllable in speech recognition output, quantizes the features, forms N-grams of the quantized values, and models normalized counts for each feature N-gram using support vector machines (SVMs). We refer to these features as “SNERF-grams” (N-grams of Syllable-based Nonuniform Extraction Region Features). Evaluation of SNERF-gram performance is conducted on two-party spontaneous English conversational telephone data from the Fisher corpus, using one conversation side in both training and testing. Results show that SNERF-grams provide significant performance gains when combined with a state-of-the-art baseline system, as well as with two highly successful long-range feature systems that capture word usage and lexically constrained duration patterns. […]
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