The SRI speaker recognition system for the 2008 NIST speaker recognition evaluation (SRE) incorporates a variety of models and features, both cepstral and stylistic. We highlight the improvements made to specific subsystems and analyze the performance of various subsystem combinations in different data conditions. We show the importance of language and nativeness conditioning, as well as the role of ASR for speaker verification. Index Terms — speaker recognition, prosody, speech recognition 1.

Recent studies in speaker recognition have shown that scorelevel combination of subsystems can yield significant performance gains over individual subsystems. We explore the use of auxiliary information to aid the combination procedure. We propose a modified linear logistic regression procedure that conditions combination weights on the auxiliary information. A regularization procedure is used to control the complexity of the extended model. Several auxiliary features are explored. Results are presented for data from the 2006 NIST speaker recognition evaluation (SRE). When an estimated degree of nonnativeness for the speaker is used as auxiliary information, the proposed combination results in a 15 % relative reduction in equal error rate over methods based on standard linear logistic regression, support vector machines, and neural networks.

In this paper, we present models for detecting various attributes of a speaker based on uttered text alone. These attributes include whether the speaker is speaking his/her native language, the speaker’s age and gender, and the regional information reported by the speakers. We explore various lexical features as well as features inspired by Linguistic Inquiry and Word Count and Dictionary of Affect in Language. Overall, results suggest that when audio data is not available, by exploring effective feature sets only from uttered text and system combinations of multiple classification algorithms, we can build high quality statistical models to detect these attributes of speakers, comparable to systems that can exploit the audio data. Index Terms: speaker attributes, machine learning, nativeness, gender, age, region

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