Recognising speakers from the topics they talk about

We investigate how a speaker's preference for specific topics can be used for speaker identification. In domains like broadcast news or parliamentary speeches, speakers have a field of expertise they are associated with. We explore how topic information for a segment of speech, extracted from an automatic speech recognition transcript, can be employed to identify the speaker. Two methods for modelling topic preferences are compared: implicitly, based on speaker-characteristic keywords, and explicitly, by using automatically derived topic models to assign topics to the speech segments. In the keyword-based approach, the segments' tf-idf vectors are classified with Support Vector Machine speaker models. For the topic-model-based approach, a domain-specific topic model is used to represent each segment as a mixture of topics; the speakers' score is derived from the Kullback-Leibler divergence between the topic mixtures of their training data and of the segment. The methods were tested on political speeches given in German parliament by 235 politicians. We found that topic cues do carry speaker information, as the topic-model-based system yielded an equal error rate (EER) of 16.3%. The topic-based approach combined well with a spectral baseline system, improving the EER from 8.6% for the spectral to 6.2% for the fused system.