DOI: 10.82270/cles-en_corpus/v1
title: Spontaneous Dialogues in L1 English
title: Spontaneous Dialogues in L1 English

description: This corpus of spontaneous L1 English comprises recordings of US university students engaging in a 10-minute role-play where two
 candidates delve into an argumentative discussion on 
contentious topics. Each candidate assumed a specific given role, either
 advocating for (role A) or against (role B) the subject. Up to 10 
minutes of preparation before the talk was allowed.Although
 participants were allowed to take notes, reading during the 
conversation was prohibited. Their objective was to negotiate, exchange 
viewpoints, and eventually work towards a compromise. This oral 
interaction task is inspired by that of the CLES English certification exam
 (CLES B2). A similar corpus with L2 English from French university students is available here.Another corpus with L2 English from Japanese university students is available here.Corpus AccessThis
 corpus is available for academic research purposes only.
Please request access by contacting the authors.ContentsThe corpus currently comprises 2 hours of speech (14 speakers, 7 recordings).For each recording, role A student on the right, role B student on the left of the microphone.See the recordings.csv and speakers.csv metadata files for more information.Date and place of recordingFebrary 2024 at Doshisha University, Kyoto, JapanRecording infoZoom Handy Recorder H2nsampling: 44.1 kHz16 bit PCM stereo1411 kb/sAutomatic Annotations of the CorpusEach recording comes with a TextGrid file with speaker segmentation. This annotation was done automatically using the Pyannote Speaker Diarization Toolkit, then manually checked.Moreover, speech segments from the TextGrid file were automatically annotated using the Pause and Lexical Stress Processing Pipeline (PLSPP), which includes:automated speech recognition and word-level alignment (WhisperX), syllable nuclei detection (De Jong et al. 2021), part-of-speech tagging (Spacy), constituency analysis (Berkeley Neural Parser), pause position analysis, polysyllabic words' lexical stress annotation.PreprocessingSound calibration: harmonization of the mean square energy by raising the volume avoiding clipping  AuthorsSylvain Coulange (Grenoble Alpes University),Takayuki Konishi (Waseda University)Tsuneo Kato (Doshisha University)Mariko Sugahara (Doshisha University)
description: This corpus of spontaneous L1 English comprises recordings of US university students engaging in a 10-minute role-play where two
 candidates delve into an argumentative discussion on 
contentious topics. Each candidate assumed a specific given role, either
 advocating for (role A) or against (role B) the subject. Up to 10 
minutes of preparation before the talk was allowed.Although
 participants were allowed to take notes, reading during the 
conversation was prohibited. Their objective was to negotiate, exchange 
viewpoints, and eventually work towards a compromise. This oral 
interaction task is inspired by that of the CLES English certification exam
 (CLES B2). A similar corpus with L2 English from French university students is available here.Another corpus with L2 English from Japanese university students is available here.Corpus AccessThis
 corpus is available for academic research purposes only.
Please request access by contacting the authors.ContentsThe corpus currently comprises 2 hours of speech (14 speakers, 7 recordings).For each recording, role A student on the right, role B student on the left of the microphone.See the recordings.csv and speakers.csv metadata files for more information.Date and place of recordingFebrary 2024 at Doshisha University, Kyoto, JapanRecording infoZoom Handy Recorder H2nsampling: 44.1 kHz16 bit PCM stereo1411 kb/sAutomatic Annotations of the CorpusEach recording comes with a TextGrid file with speaker segmentation. This annotation was done automatically using the Pyannote Speaker Diarization Toolkit, then manually checked.Moreover, speech segments from the TextGrid file were automatically annotated using the Pause and Lexical Stress Processing Pipeline (PLSPP), which includes:automated speech recognition and word-level alignment (WhisperX), syllable nuclei detection (De Jong et al. 2021), part-of-speech tagging (Spacy), constituency analysis (Berkeley Neural Parser), pause position analysis, polysyllabic words' lexical stress annotation.PreprocessingSound calibration: harmonization of the mean square energy by raising the volume avoiding clipping  AuthorsSylvain Coulange (Grenoble Alpes University),Takayuki Konishi (Waseda University)Tsuneo Kato (Doshisha University)Mariko Sugahara (Doshisha University)