82 citations · 92 across the 5 of their papers we have counts for
5 papers
Alzheimer's Dementia Recognition Using Acoustic, Lexical, Disfluency and Speech Pause Features Robust to Noisy Inputs
Morteza Rohanian, Julian Hough, Matthew Purver
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 diagn…
Multi-modal fusion with gating using audio, lexical and disfluency features for Alzheimer's Dementia recognition from spontaneous speech
Morteza Rohanian, Julian Hough, Matthew Purver
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 pre…
Re-framing Incremental Deep Language Models for Dialogue Processing with Multi-task Learning
Morteza Rohanian, Julian Hough
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…
Exploring Semantic Incrementality with Dynamic Syntax and Vector Space Semantics
Mehrnoosh Sadrzadeh, Matthew Purver, Julian Hough +1
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 w…
Strongly Incremental Repair Detection
Julian Hough, Matthew Purver
We present STIR (STrongly Incremental Repair detection), a system that detects speech repairs and edit terms on transcripts incrementally with minimal latency. STIR uses informatio…