David A. van Leeuwen

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adaptation or nuisance attribute projection. We document the design and performance of all subsystems , as well as their fusion and calibration via logistic regression. Finally, we also present a cross-site fusion that was done with several additional systems from other NIST SRE-2006 participants.
Emotions can be recognized by audible paralinguistic cues in speech. By detecting these paralinguistic cues that can consist of laughter , a trembling voice, coughs, changes in the intonation contour etc., information about the speaker's state and emotion can be revealed. This paper describes the development of a gender-independent laugh detector with the(More)
In this paper we describe the AMIDA speaker dizarization system as it was submitted to the NIST Rich Transcription evaluation 2007 for conference room data. This is done in the context of the history of this system and other speaker diarization systems. One of the goals of our system is to have as little tunable parameters as possible, while maintaining(More)
We describe the systems submitted to the NIST RT06s evaluation for the Speech Activity Detection (SAD) and Speaker Diarization (SPKR) tasks. For speech activity detection, a new analysis methodology is presented that generalizes the Detection Erorr Tradeoff analysis commonly used in speaker detection tasks. The speaker diarization systems are based on the(More)
Speaker recognition systems trained on long duration utterances are known to perform significantly worse when short test segments are encountered. To address this mismatch, we analyze the effect of duration variability on phoneme distributions of speech utterances and i-vector length. We demonstrate that, as utterance duration is decreased , number of(More)
This paper investigates the task of linking speakers across multiple recordings, which can be accomplished by speaker clustering. Various aspects are considered, such as computational complexity, on/offline approaches, and evaluation measures but also speaker recognition approaches. It has not been the aim of this study to optimize clustering performance,(More)
In this paper we present a method for automatically generating acoustic sub-word units that can substitute conventional phone models in a query-by-example spoken term detection system. We generate the sub-word units with a modified version of our speaker diarization system. Given a speech recording, the original diariza-tion system generates a set of(More)