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Found 18 papers, 2 papers with code
Rare-Class Dialogue Act Tagging for Alzheimer’s Disease Diagnosis
Alzheimer’s Disease (AD) is associated with many characteristic changes, not only in an individual’s language but also in the interactive patterns observed in dialogue.
Communicative Grounding of Analogical Explanations in Dialogue: A Corpus Study of Conversational Management Acts and Statistical Sequence Models for Tutoring through Analogy
We annotate a corpus of analogical episodes with the schema and develop statistical sequence models from the corpus which predict tutor content related decisions, in terms of the selection of the analogical component (AC) and tutor conversational management act (TCMA) to deploy at the current utterance, given the student’s behaviour.
Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental
While Transformer-based text classifiers pre-trained on large volumes of text have yielded significant improvements on a wide range of computational linguistics tasks, their implementations have been unsuitable for live incremental processing thus far, operating only on the level of complete sentence inputs.
Alzheimer's Dementia Recognition Using Acoustic, Lexical, Disfluency and Speech Pause Features Robust to Noisy Inputs
We present two multimodal fusion-based deep learning models that consume ASR transcribed speech and acoustic data simultaneously to classify whether a speaker in a structured diagnostic task has Alzheimer's Disease and to what degree, evaluating the ADReSSo challenge 2021 data.
Multi-modal fusion with gating using audio, lexical and disfluency features for Alzheimer's Dementia recognition from spontaneous speech
This paper is a submission to the Alzheimer's Dementia Recognition through Spontaneous Speech (ADReSS) challenge, which aims to develop methods that can assist in the automated prediction of severity of Alzheimer's Disease from speech data.
Re-framing Incremental Deep Language Models for Dialogue Processing with Multi-task Learning
We present a multi-task learning framework to enable the training of one universal incremental dialogue processing model with four tasks of disfluency detection, language modelling, part-of-speech tagging, and utterance segmentation in a simple deep recurrent setting.
Exploring Semantic Incrementality with Dynamic Syntax and Vector Space Semantics
One of the fundamental requirements for models of semantic processing in dialogue is incrementality: a model must reflect how people interpret and generate language at least on a word-by-word basis, and handle phenomena such as fragments, incomplete and jointly-produced utterances.
Natural Language Informs the Interpretation of Iconic Gestures: A Computational Approach
When giving descriptions, speakers often signify object shape or size with hand gestures.
Joint, Incremental Disfluency Detection and Utterance Segmentation from Speech
We present the joint task of incremental disfluency detection and utterance segmentation and a simple deep learning system which performs it on transcripts and ASR results.
PentoRef: A Corpus of Spoken References in Task-oriented Dialogues
PentoRef is a corpus of task-oriented dialogues collected in systematically manipulated settings.
DUEL: A Multi-lingual Multimodal Dialogue Corpus for Disfluency, Exclamations and Laughter
We present the DUEL corpus, consisting of 24 hours of natural, face-to-face, loosely task-directed dialogue in German, French and Mandarin Chinese.
Strongly Incremental Repair Detection
We present STIR (STrongly Incremental Repair detection), a system that detects speech repairs and edit terms on transcripts incrementally with minimal latency.
