Multimodal Speaker Identification Based on Text and Speech

This paper proposes a novel method for speaker identification based on both speech utterances and their transcribed text. The transcribed text of each speaker's utterance is processed by the probabilistic latent semantic indexing (PLSI) that offers a powerful means to model each speaker's vocabulary employing a number of hidden topics, which are closely related to his/her identity, function, or expertise. Mel-frequency cepstral coefficients (MFCCs) axe extracted from each speech frame and their dynamic range is quantized to a number of predefined bins in order to compute MFCC local histograms for each speech utterance, which is time-aligned with the transcribed text. Two identity scores are independently computed by the PLSI applied to the text and the nearest neighbor classifier applied to the local MFCC histograms. It is demonstrated that a convex combination of the two scores is more accurate than the individual scores on speaker identification experiments conducted on broadcast news of the RT-03 MDE Training Data Text and Annotations corpus distributed by the Linguistic Data Consortium.