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We report on machine learning experiments to distinguish deceptive from nondeceptive speech in the Columbia-SRI-Colorado (CSC) corpus. Specifically, we propose a system combination approach using different models and features for deception detection. Scores from an SVM system based on prosodic/lexical features are combined with scores from a Gaussian(More)
To date, studies of deceptive speech have largely been confined to descriptive studies and observations from subjects, researchers , or practitioners, with few empirical studies of the specific lexical or acoustic/prosodic features which may characterize deceptive speech. We present results from a study seeking to distinguish deceptive from non-deceptive(More)
Previous studies of human performance in deception detection have found that humans generally are quite poor at this task, comparing unfavorably even to the performance of automated procedures. However, different scenarios and speakers may be harder or easier to judge. In this paper we compare human to machine performance detecting deception on a single(More)
— We summarize recent progress in automatic speech-to-text transcription at SRI, ICSI, and the University of Washing-ton. The work encompasses all components of speech modeling found in a state-of-the-art recognition system, from acoustic features , to acoustic modeling and adaptation, to language modeling. In the front end, we experimented with nonstandard(More)
Background noise and channel degradations seriously constrain the performance of state-of-the-art speech recognition systems. Studies comparing human speech recognition performance with automatic speech recognition systems indicate that the human auditory system is highly robust against background noise and channel variabilities compared to automated(More)
This article describes our submission to the speaker identification (SID) evaluation for the first phase of the DARPA Robust Audio and Transcription of Speech (RATS) program. The evaluation focuses on speech data heavily degraded by channel effects. We show here how we designed a robust system using multiple streams of noise-robust features that were(More)
Deep Neural Network (DNN) based acoustic models have shown significant improvement over their Gaussian Mixture Model (GMM) counterparts in the last few years. While several studies exist that evaluate the performance of GMM systems under noisy and channel degraded conditions, noise robustness studies on DNN systems have been far fewer. In this work we(More)
This work addresses the problem of speaker verification where additive noise is present in the enrollment and testing utterances. We show how the current state-of-the-art framework can be effectively used to mitigate this effect. We first look at the degradation a standard speaker verification system is subjected to when presented with noisy speech(More)
We study the question of how intrinsic variations (associated with the speaker rather than the recording environment) affect text-independent speaker verification performance. Experiments using the SRI-FRTIV corpus, which systematically varies both vocal effort and speaking style, reveal that (1) " furtive " speech poses a significant challenge; (2)(More)
—We introduce a new database for evaluation of speaker recognition systems. This database involves types of variability already seen in NIST speaker recognition evaluations (SREs) like language, channel, speech style and vocal effort, and new types not yet available on any standard database like severe noise, and reverberation. The database is created using(More)