An efficient scheme, based on the Bayesian information criterion (BIC), for the detection of speaker changes in an audio stream is introduced and investigated. BIC has been the subject of considerable attention in recent years due to its effectiveness for speaker change detection (SCD) as well as the detection of other forms of acoustic changes. A main difficulty in BIC-based SCD has been repor...

In this paper, we are interested in detecting changes in speaker identity, environmental condition and channel condition; we call this the problem of acoustic change detection. The input audio stream can be modeled as a Gaussian process in the cepstral space. We present a maximum likelihood approach to detect turns of a Gaussian process; the decision of a turn is based on the Bayesian Informati...

Model selection is an important problem in statistical applications. Bayesian model averaging provides an alternative to classical model selection procedures and allows researchers to consider several models from which to draw inferences. In the multiple linear regression case, it is di4cult to compute exact posterior model probabilities required for Bayesian model averaging. To reduce the comp...

We use an exact Bayesian calculation to design classifiers that distinguish whether a finite sequence drawn from a finite alphabet is a sample path of a Markov chain of order k = 0 or of order k > 0. Three exact Bayes (EB) classifiers are derived, each corresponding to a different prior. We also include a classifier based on the Bayesian Information Criterion (BIC), a popular technique for Mark...

Previous work examined the suitability of relying on routine methods model selection when extrapolating survival data in a health technology appraisal setting. Here we explore solutions to improve reliability restricted mean time (RMST) estimates from trial by assessing plausibility and implementing averaging. We compare our previous selecting for extrapolation using Akaike information criterio...

Speaker segmentation systems usually have problems detecting short segments, which causes the number of deletions to be high and therefore harming the performance of the system. This is a complication when it comes to segmenting multimedia information such as movies and TV shows, where dialogs among characters are very common. In this paper a modification of the BIC algorithm is presented, whic...

One of the most popular criteria for model selection is the Bayesian Information Criterion (BIC). It is based on an asymptotic approximation using Bayes rule when the sample size tends to infinity and the dimension of the model is fixed. Although it works well in classical applications, it performs less satisfactorily for high dimensional problems, i.e. when the number of regressors is very lar...

Behavioural sleep problems, that is, sleep problems that do not have a physiological aetiology, but rather a behavioural or psychological aetiology, are reported in between 20% 40% of children and adolescents. These sleep disorders are categorised as Behavioural Insomnia of Childhood (BIC) in the International Classification of Sleep Disorders. BIC can result in short sleep duration and poor qu...

Résumé. Nous reconsidérons dans cet article le critère BIC pour arbres d’induction proposé dans Ritschard et Zighed (2003, 2004) et discutons deux aspects liés à sa portée. Le premier concerne les possibilités de le calculer. Nous montrons comment il s’obtient à partir des statistiques du rapport vraisemblance utilisées pour tester l’indépendance ligne-colonne de tables de contingence. Le secon...

For a given gene, the total number of potential predictors p in BBSR is determined by the size of the union of the 10 highest scoring predictors based on tlCLR and all those predictors that have previously been reported as regulators (see Method section in main article). If p is large (> 10) it becomes infeasible to compute all 2 possible regression models during the model selection step. To fu...