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Found 13 papers, 0 papers with code
ASR Performance Prediction on Unseen Broadcast Programs using Convolutional Neural Networks
In this paper, we address a relatively new task: prediction of ASR performance on unseen broadcast programs.
Comparaison de listes d'erreurs de transcription automatique de la parole : quelle compl\'ementarit\'e entre les diff\'erentes m\'etriques ? (Comparing error lists for ASR systems : contribution of different metrics)
Le travail que nous pr{\'e}sentons ici s{'}inscrit dans le domaine de l{'}{\'e}valuation des syst{\`e}mes de reconnaissance automatique de la parole en vue de leur utilisation dans une t{\^a}che aval, ici la reconnaissance des entit{\'e}s nomm{\'e}es.
LNE-Visu : a tool to explore and visualize multimedia data
LNE-Visu : a tool to explore and visualize multimedia data LNE-Visu is a tool to explore and visualize multimedia data created for the LNE evaluation campaigns.
Generating Task-Pertinent sorted Error Lists for Speech Recognition
In the context of Automatic Speech Recognition (ASR) used as a first step towards Named Entity Recognition (NER) in speech, error seriousness is usually determined by their frequency, due to the use of the WER as metric to evaluate the ASR output, despite the emergence of more relevant measures in the literature.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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FABIOLE, a Speech Database for Forensic Speaker Comparison
A speech database has been collected for use to highlight the importance of {``}speaker factor{''} in forensic voice comparison.
ETER : a new metric for the evaluation of hierarchical named entity recognition
We then introduce a new metric, the Entity Tree Error Rate (ETER), to evaluate hierarchical and structured named entity detection, classification and decomposition.
Human annotation of ASR error regions: Is ``gravity'' a sharable concept for human annotators?
This paper is concerned with human assessments of the severity of errors in ASR outputs.
The REPERE Corpus : a multimodal corpus for person recognition
In this context, the REPERE corpus, a French videos corpus with multimodal annotation, has been developed.
