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Found 18 papers, 0 papers with code
An audiovisual political speech analysis incorporating eye-tracking and perception data
Additionally, we compare the gaze behavior of the human subjects to evaluate saliency regions in the multimodal and visual only conditions.
The Herme Database of Spontaneous Multimodal Human-Robot Dialogues
This paper presents methodologies and tools for language resource (LR) construction.
The D-ANS corpus: the Dublin-Autonomous Nervous System corpus of biosignal and multimodal recordings of conversational speech
We believe this corpus is particularly relevant to researchers interested in unscripted social conversation as well as to researchers with a specific interest in observing the dynamics of biosignals during informal social conversation rich with examples of laughter, conversational turn-taking, and non-task-based interaction.
Capturing Chat: Annotation and Tools for Multiparty Casual Conversation.
Casual multiparty conversation is an understudied but very common genre of spoken interaction, whose analysis presents a number of challenges in terms of data scarcity and annotation.
The ILMT-s2s Corpus ― A Multimodal Interlingual Map Task Corpus
The corpus design is inspired by the HCRC Map Task Corpus which was initially designed to support the investigation of linguistic phenomena, and has been the focus of a variety of studies of communicative behaviour.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
CHATR the Corpus; a 20-year-old archive of Concatenative Speech Synthesis
This paper reports the preservation of an old speech synthesis website as a corpus.
Incorporating Global Visual Features into Attention-Based Neural Machine Translation
We introduce multi-modal, attention-based neural machine translation (NMT) models which incorporate visual features into different parts of both the encoder and the decoder.
Ranked #10 on
Multimodal Machine Translation
on Multi30K
Multilingual Multi-modal Embeddings for Natural Language Processing
We propose a novel discriminative model that learns embeddings from multilingual and multi-modal data, meaning that our model can take advantage of images and descriptions in multiple languages to improve embedding quality.
Doubly-Attentive Decoder for Multi-modal Neural Machine Translation
We introduce a Multi-modal Neural Machine Translation model in which a doubly-attentive decoder naturally incorporates spatial visual features obtained using pre-trained convolutional neural networks, bridging the gap between image description and translation.
Ranked #11 on
Multimodal Machine Translation
on Multi30K
Just Talking - Modelling Casual Conversation
Casual conversation has become a focus for artificial dialogue applications.
