Search Results for author:
Found 24 papers, 6 papers with code
Seq2seq for Automatic Paraphasia Detection in Aphasic Speech
In this paper, we propose a novel, sequence-to-sequence (seq2seq) model that is trained end-to-end (E2E) to perform both ASR and paraphasia detection tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Automatic Disfluency Detection from Untranscribed Speech
We find that disfluency detection performance is largely limited by the quality of transcripts and alignments.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Articulatory Coordination for Speech Motor Tracking in Huntington Disease
Lastly, we analyze the F-value scores of VTC features to visualize which channels are most related to motor score.
Accounting for Variations in Speech Emotion Recognition with Nonparametric Hierarchical Neural Network
In recent years, deep-learning-based speech emotion recognition models have outperformed classical machine learning models.
Learning Paralinguistic Features from Audiobooks through Style Voice Conversion
Paralinguistics, the non-lexical components of speech, play a crucial role in human-human interaction.
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems
We end the paper with a set of recommendations for noise augmentations in speech emotion recognition datasets.
Human-Imitating Metrics for Training and Evaluating Privacy Preserving Emotion Recognition Models Using Sociolinguistic Knowledge
In this paper, we propose an automatic and quantifiable metric that allows us to evaluate humans' perception of a model's ability to preserve privacy with respect to sensitive variables.
Dynamic Layer Customization for Noise Robust Speech Emotion Recognition in Heterogeneous Condition Training
Robustness to environmental noise is important to creating automatic speech emotion recognition systems that are deployable in the real world.
Quantifying the Effects of COVID-19 on Mental Health Support Forums
The COVID-19 pandemic, like many of the disease outbreaks that have preceded it, is likely to have a profound effect on mental health.
Classification of Huntington Disease using Acoustic and Lexical Features
Speech is a critical biomarker for Huntington Disease (HD), with changes in speech increasing in severity as the disease progresses.
MuSE: a Multimodal Dataset of Stressed Emotion
Endowing automated agents with the ability to provide support, entertainment and interaction with human beings requires sensing of the users{'} affective state.
Privacy Enhanced Multimodal Neural Representations for Emotion Recognition
In this work, we show how multimodal representations trained for a primary task, here emotion recognition, can unintentionally leak demographic information, which could override a selected opt-out option by the user.
Identifying Mood Episodes Using Dialogue Features from Clinical Interviews
Bipolar disorder, a severe chronic mental illness characterized by pathological mood swings from depression to mania, requires ongoing symptom severity tracking to both guide and measure treatments that are critical for maintaining long-term health.
When to Intervene: Detecting Abnormal Mood using Everyday Smartphone Conversations
Predicting when to intervene is challenging because there is not a single measure that is relevant for every person: different individuals may have different levels of symptom severity considered typical.
The Ambiguous World of Emotion Representation
A key reason for this is the lack of a common mathematical framework to describe all the relevant elements of emotion representations.
Controlling for Confounders in Multimodal Emotion Classification via Adversarial Learning
Our results show that stress is indeed encoded in trained emotion classifiers and that this encoding varies across levels of emotions and across the lexical and acoustic modalities.
Jointly Aligning and Predicting Continuous Emotion Annotations
To deal with this challenge, we introduce a new convolutional neural network (multi-delay sinc network) that is able to simultaneously align and predict labels in an end-to-end manner.
Improving Cross-Corpus Speech Emotion Recognition with Adversarial Discriminative Domain Generalization (ADDoG)
We also show how, in most cases, ADDoG and MADDoG can be used to improve upon baseline state-of-the-art methods when target dataset labels are added and in-the-wild data are considered.
MuSE-ing on the Impact of Utterance Ordering On Crowdsourced Emotion Annotations
As a result, annotations are colored by the manner in which they were collected.
Trainable Time Warping: Aligning Time-Series in the Continuous-Time Domain
We introduce trainable time warping (TTW), whose complexity is linear in both the number and the length of time-series.
Improving End-of-turn Detection in Spoken Dialogues by Detecting Speaker Intentions as a Secondary Task
This work focuses on the use of acoustic cues for modeling turn-taking in dyadic spoken dialogues.
Capturing Long-term Temporal Dependencies with Convolutional Networks for Continuous Emotion Recognition
The goal of continuous emotion recognition is to assign an emotion value to every frame in a sequence of acoustic features.
Progressive Neural Networks for Transfer Learning in Emotion Recognition
Many paralinguistic tasks are closely related and thus representations learned in one domain can be leveraged for another.
MSP-IMPROV: An Acted Corpus of Dyadic Interactions to Study Emotion Perception
The paper also provides the performance for speech and facial emotion classifiers.
