CONF lathoud06b/IDIAP Threshold Selection for Unsupervised Detection, with an Application to Microphone Arrays Lathoud, Guillaume Magimai-Doss, Mathew Bourlard, Hervé EXTERNAL http://publications.idiap.ch/attachments/papers/2006/lathoud06b.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/lathoud-rr-05-52 Related documents Proceedings of ICASSP 2006 2006 Toulouse, France May 2006 IDIAP RR 05-52 Detection is usually done by comparing some criterion to a threshold. It is often desirable to keep a performance metric such as False Alarm Rate constant across conditions. Using training data to select the threshold may lead to suboptimal results on test data recorded in different conditions. This paper investigates unsupervised approaches, where no training data is used. A probabilistic model is fitted on the test data using the EM algorithm, and the threshold value is selected based on the model. The proposed approach (1) does not use training data, (2) uses the test data itself to compensate for simplifications inherent to the model, (3) permits the use of more complex models in a straightforward manner. On a microphone array speech detection task, the proposed unsupervised approach achieves similar or better results than the ``training'' approach. The methodology is general and may be applied to other contexts than microphone arrays, and other performance metrics than FAR. REPORT lathoud-rr-05-52/IDIAP Threshold Selection for Unsupervised Detection, with an Application to Microphone Arrays Lathoud, Guillaume Magimai-Doss, Mathew Odobez, Jean-Marc Bourlard, Hervé EXTERNAL http://publications.idiap.ch/attachments/reports/2005/rr-05-52.pdf PUBLIC Idiap-RR-52-2005 2005 IDIAP Martigny, Switzerland Published in the Proceedings of ICASSP 2005 Detection is usually done by comparing some criterion to a threshold. It is often desirable to keep a performance metric such as False Alarm Rate constant across conditions. Using training data to select the threshold may lead to suboptimal results on test data recorded in different conditions. This paper investigates unsupervised approaches, where no training data is used. A probabilistic model is fitted on the test data using the EM algorithm, and the threshold value is selected based on the model. The proposed approach (1)~does not use training data, (2)~uses the test data itself to compensate simplifications inherent to the model, (3)~permits the use of more complex models in a straightforward manner. On a microphone array speech detection task, the proposed unsupervised approach achieves similar or better results than the ``training'' approach. The methodology is general and may be applied to other contexts than microphone arrays, and other performance metrics than FAR.