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Pierre Jourlin

dblp:59/2799 · DBLP profile ↗
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7ranked-venue papers
4as first author
0since 2021 · last 2000
0000-0002-8857-5436ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 3 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 3 · 2 first-authorArtificial intelligence and machine learning · 2 · 2 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
3 papers
Information retrieval · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval › document retrieval
spoken document retrieval		 0.122000
Effects of out of vocabulary words in spoken document retrieval · SIGIR 2000
Improving Retrieval on Imperfect Speech Transcriptions (poster abstract) · SIGIR 1999
Information retrieval
multimedia analysis and retrieval		 0.012000
The Cambridge University multimedia document retrieval demo system · SIGIR 2000
Information retrieval › query reformulation
query expansion		 0.012000
Effects of out of vocabulary words in spoken document retrieval · SIGIR 2000

Methods — techniques the papers use, named apart from their topics

speech recognition · 0.0
YearPublicationVenuePosition
2000 The Cambridge University multimedia document retrieval demo system
abstract
No abstract available.
Andreas Tuerk, Sue Tranter, Pierre Jourlin, Karen Spärck Jones, Philip C. Woodland
 SIGIR3
2000 Effects of out of vocabulary words in spoken document retrieval
abstract
The effects of out-of-vocabulary (OOV) items in spoken document retrieval (SDR) are investigated. Several sets of transcriptions were created for the TREC-8 SDR task using a speech recognition system varying the vocabulary sizes and OOV rates, and the relative retrieval performance measured. The effects of OOV terms on a simple baseline IR system and on more sophisticated retrieval systems are described. The use of a parallel corpus for query and document expansion is found to be especially beneficial, and with this data set, good retrieval performance can be achieved even for fairly high OOV rates.
Philip C. Woodland, Sue Tranter, Pierre Jourlin, Karen Spärck Jones
 SIGIR3
2000 Spoken document representations for probabilistic retrieval
Pierre Jourlin, Sue Tranter, Karen Spärck Jones, Philip C. Woodland
 Speech Commun.1
1999 The Cambridge University spoken document retrieval system
abstract
This paper describes the spoken document retrieval system that we have been developing and assesses its performance using automatic transcriptions of about 50 hours of broadcast news data. The recognition engine is based on the HTK broadcast news transcription system and the retrieval engine is based on the techniques developed at City University. The retrieval performance over a wide range of speech transcription error rates is presented and a number of recognition error metrics that more accurately reflect the impact of transcription errors on retrieval accuracy are defined and computed. The results demonstrate the importance of high accuracy automatic transcription. The final system is currently being evaluated on the 1998 TREC-7 spoken document retrieval task.
Sue Tranter, Pierre Jourlin, Gareth L. Moore, Karen Spärck Jones, Philip C. Woodland
 ICASSP2
1999 Improving Retrieval on Imperfect Speech Transcriptions (poster abstract)
abstract
No abstract available.
Pierre Jourlin, Sue Tranter, Karen Spärck Jones, Philip C. Woodland
 SIGIR1
1997 Integrating acoustic and labial information for speaker identification and verification
abstract
LIDIAP
Pierre Jourlin, Jürgen Lüttin, Dominique Genoud, Hubert Wassner
 EUROSPEECH1
1997 Acoustic-labial speaker verification
abstract
This paper describes a multimodal approach for speaker verification. The system consists of two classifiers, one using visual features, the other using acoustic features. A lip tracker is used to extract visual information from the speaking face which provides shape and intensity features. We describe an approach for normalizing and mapping different modalities onto a common confidence interval. We also describe a novel method for integrating the scores of multiple classifiers. Verification experiments are reported for the individual modalities and for the combined classifier. The integrated system outperformed each sub-system and reduced the false acceptance rate of the acoustic sub-system from 2.3% to 0.5%.
Pierre Jourlin, Jürgen Lüttin, Dominique Genoud, Hubert Wassner
 Pattern Recognit. Lett.1