The ETAPE corpus for the evaluation of speech-based TV content processing in the French language

By Guillaume Gravier, Gilles Adda, Niklas Paulsson, Matthieu Carré, Aude Giraudel, Olivier Galibert. 2012

196 citations recorded by the source corpus.

The paper presents a comprehensive overview of existing data for the evaluation of spoken content processing in a multimedia framework for the French language. We focus on the ETAPE corpus which will be made publicly available by ELDA mid 2012, after completion of the evaluation campaign, and recall existing resources resulting from previous evaluation campaigns. The ETAPE corpus consists of 30 hours of TV and radio broadcasts, selected to cover a wide variety of topics and speaking styles, emphasizing spontaneous speech and multiple speaker areas.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Evaluation & Benchmarks, Speech & Audio

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Citations in the corpus

An open-source state-of-the-art toolbox for broadcast news diarization (2013, result): “The scoring tool we used was developed by the LNE 5 as part of the ETAPE and the REPERE campaigns.”
Speaker diarization through speaker embeddings (2015, methodology): “In the following experiments, we use the ETAPE 2012 data [18].”
AVA-Speech: A Densely Labeled Dataset of Speech Activity in Movies (2018, background): “Datasets from movies and TV shows—ETAPE corpus (7 hours from 15 TV shows) [21], REPERE corpus (3 hours from 28 TV shows) [22], 4 Hollywood movies in [23] —comes closest to satisfying the characteristics described above, and are fairly close in the style of content to the labeled dataset that we cont”
The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines (2018, background): “This can be attributed to advances in speech processing, audio enhancement, and machine learning, but also to the availability of real speech corpora recorded in cars [6, 7], quiet indoor environments [8, 9], noisy indoor and outdoor environments [10, 11], and challenging broadcast media [12,13].”
VoiceHome-2, an extended corpus for multichannel speech processing in real homes (2019, unknown): “…(Janin et al., 2003; Mostefa et al., 2007; Renals et al., 2008), dialogs (LLSEC, 1996; Stupakov et al., 2011) and other public gatherings (Lincoln et al., 2005; Fox et al., 2013), and automatic transcription of noisy or overlapped speech in broadcast media (Gravier et al., 2012; Bell et al., 2015).”
A French Corpus for Distant-Microphone Speech Processing in Real Homes (2016, unknown): “A number of real corpora are now publicly available for environments and application scenarios such as voice command for cars [7–9] and in public spaces [10], automatic transcription of lectures [11], meetings [12–14], dialogues [15, 16] and other public gatherings [17, 18], and automatic transcript”
Analyzing Learned Representations of a Deep ASR Performance Prediction Model (2018, methodology): “Speech utterances come from various French broadcast collections gathered during projects or shared tasks: Quaero, ETAPE, ESTER 1 & ESTER 2 and REPERE.”