The eNTERFACE'05 Audio-Visual Emotion Database

By Olivier Martin, Irene Kotsia, Benoit M. Macq, Ioannis Pitas. 2006

405 citations recorded by the source corpus.

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Citations in the corpus

Expectation Learning for Adaptive Crossmodal Stimuli Association (2018, methodology): “We use the Enterface05 audio-visual challenge database [16] which contains recordings of 44 different subjects which are speaking phrases in different emotion intonations.”
Learning Affective Features With a Hybrid Deep Model for Audio–Visual Emotion Recognition (2018, background): “…of our proposed hybrid deep learning networks for audio-visual emotion recognition, we conduct emotion recognition experiments on three public audio-visual emotional datasets, including the acted RML dataset [3], the acted eNTERFACE05 dataset [59], and the spontaneous BAUM-1s dataset [12].”
Sparse Autoencoder-Based Feature Transfer Learning for Speech Emotion Recognition (2013, background): “…highly popular databases, namely the TUM Audio-Visual Interest Corpus (TUM AVIC) [16], Berlin Emotional Speech Database (EMO-DB) [17], eNTERFACE [18], Speech Under Simulated and Actual Stress (SUSAS) [19], and the “Vera am Mittag” (VAM) database [17], [20] are chosen as source set, which are…”
Deep learning in mental health outcome research: a scoping review (2020, background): “For example, some studies trained autoencoder in public emotion database such as eNTERFACE 95 to generate emotion pro ﬁ les (EPs).”
A comprehensive survey on feature selection in the various fields of machine learning (2021, methodology): “IEMOCAP [163], EMO-DB [164], SAVEE [165], CASIA [166], eNTERFACE’05 [167], EMOVO [168], MASC [169] Speech Emotion Recognition Table 18 The description of various types of features used in the FS task for speech processing applications”
MSP-IMPROV: An Acted Corpus of Dyadic Interactions to Study Emotion Perception (2017, methodology): “…recording the scenarios for the first 10 sentences, the actors switched roles and A preliminary study shows rats to be more inquisitive than once thought 9 That's it, the meeting is finished 10 I don't know how she could miss this opportunity Person A: Your friend is lazy and rarely goes to class.”
Recent Advances and Trends in Multimodal Deep Learning: A Review (2021, background): “eNTERFACE audio-visual database [96] contains 1,166 video sequences.”
Emotional Voice Conversion: Theory, Databases and ESD (2021, background): “eNTERFACE’05 (Martin et al., 2006) EN Angry, disgust, fear, happy, sad, surprise 42 speakers (34 male and 8 female) from 14 nationalities, 1116 video clips in total Audio/Visual Improvised With different accents Limited lexical variability”
Emotion Recognition in Speech using Cross-Modal Transfer in the Wild (2018, methodology): “eNTERFACE [47]: The eNTERFACE dataset is an acted dataset (in English) recorded in a studio.”
Minimax Filter: Learning to Preserve Privacy from Inference Attacks (2016, background): “Emotion classification from speech: The ENTERFACE database (Martin et al., 2006) is an audiovisual emotion database of 43 speakers from 14 nations reading predefined English sentences in six induced emotions.”