P
Pietro Laface
Researcher at Polytechnic University of Turin
Publications - 120
Citations - 2904
Pietro Laface is an academic researcher from Polytechnic University of Turin. The author has contributed to research in topics: Speaker recognition & Vocabulary. The author has an hindex of 27, co-authored 120 publications receiving 2682 citations. Previous affiliations of Pietro Laface include University of Salzburg & Olivetti.
Papers
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Journal ArticleDOI
Automatic speech recognition and speech variability: A review
Mohamed Faouzi BenZeghiba,R. De Mori,Olivier Deroo,Stéphane Dupont,T. Erbes,D. Jouvet,Luciano Fissore,Pietro Laface,Alfred Mertins,Christophe Ris,Richard Rose,Vivek Tyagi,Christian Wellekens +12 more
TL;DR: Current advances related to automatic speech recognition (ASR) and spoken language systems and deficiencies in dealing with variation naturally present in speech are outlined.
Journal ArticleDOI
Linear hidden transformations for adaptation of hybrid ANN/HMM models
TL;DR: The results show that the proposed approach always outperforms the use of transformations in the feature space and yields even better results when combined with linear input transformations.
Proceedings ArticleDOI
A fast segmental Viterbi algorithm for large vocabulary recognition
Pietro Laface,C. Vair,L. Fissore +2 more
TL;DR: A new search strategy particularly effective for very large vocabulary word recognition, performs a tree based, time synchronous, left-to-right beam search that develops time-dependent acoustic and phonetic hypotheses.
Journal ArticleDOI
Pairwise Discriminative Speaker Verification in the ${\rm I}$ -Vector Space
TL;DR: It is shown that it is possible to train a gender-independent discriminative model that achieves state-of-the-art accuracy, comparable to the one of aGender-dependent system, saving memory and execution time both in training and in testing.
Proceedings ArticleDOI
Stream-based speaker segmentation using speaker factors and eigenvoices
TL;DR: A stream-based approach for unsupervised multi-speaker conversational speech segmentation that produces segmentation error rates better than the state of the art ones reported in previous work on the segmentation task in the NIST 2000 Speaker Recognition Evaluation (SRE).