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Pierre Dumouchel
Researcher at École de technologie supérieure
Publications - 104
Citations - 8484
Pierre Dumouchel is an academic researcher from École de technologie supérieure. The author has contributed to research in topics: Speaker recognition & Speaker diarisation. The author has an hindex of 28, co-authored 104 publications receiving 7792 citations. Previous affiliations of Pierre Dumouchel include Université du Québec & École Normale Supérieure.
Papers
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Journal ArticleDOI
Front-End Factor Analysis for Speaker Verification
TL;DR: An extension of the previous work which proposes a new speaker representation for speaker verification, a new low-dimensional speaker- and channel-dependent space is defined using a simple factor analysis, named the total variability space because it models both speaker and channel variabilities.
Journal ArticleDOI
Joint Factor Analysis Versus Eigenchannels in Speaker Recognition
TL;DR: It is shown how the two approaches to the problem of session variability in Gaussian mixture model (GMM)-based speaker verification, eigenchannels, and joint factor analysis can be implemented using essentially the same software at all stages except for the enrollment of target speakers.
Journal ArticleDOI
A Study of Interspeaker Variability in Speaker Verification
TL;DR: It is shown that when a large joint factor analysis model is trained in this way and tested on the core condition, the extended data condition and the cross-channel condition, it is capable of performing at least as well as fusions of multiple systems of other types.
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Eigenvoice modeling with sparse training data
TL;DR: This work derives an exact solution to the problem of maximum likelihood estimation of the supervector covariance matrix used in extended MAP (or EMAP) speaker adaptation and shows how it can be regarded as a new method of eigenvoice estimation.
Proceedings Article
Support vector machines versus fast scoring in the low-dimensional total variability space for speaker verification
TL;DR: A new speaker verification system architecture based on Joint Factor Analysis (JFA) as feature extractor is presented, using the use of the cosine kernel in the new total factor space to design two different systems: the first system is Support Vector Machines based, and the second one uses directly this kernel as a decision score.