Search Results for author:
Found 22 papers, 12 papers with code
Theano: A Greek-speaking conversational agent for COVID-19
Conversational Agents (CAs) can be a proxy for disseminating information and providing support to the public, especially in times of crisis.
Predicting positive transfer for improved low-resource speech recognition using acoustic pseudo-tokens
Continued pre-training on 70-200 hours of untranscribed speech in these languages can help -- but what about languages without that much recorded data?
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Investigating Personalization Methods in Text to Music Generation
In this work, we investigate the personalization of text-to-music diffusion models in a few-shot setting.
Weakly-supervised forced alignment of disfluent speech using phoneme-level modeling
The study of speech disorders can benefit greatly from time-aligned data.
Depression detection in social media posts using affective and social norm features
Motivated by psychological literature we propose to incorporate profanity and morality features of posts and words in our architecture using a late fusion scheme.
Sample-Efficient Unsupervised Domain Adaptation of Speech Recognition Systems A case study for Modern Greek
Modern speech recognition systems exhibits rapid performance degradation under domain shift.
Adapted Multimodal BERT with Layer-wise Fusion for Sentiment Analysis
Multimodal learning pipelines have benefited from the success of pretrained language models.
Extending Compositional Attention Networks for Social Reasoning in Videos
We propose a novel deep architecture for the task of reasoning about social interactions in videos.
Regotron: Regularizing the Tacotron2 architecture via monotonic alignment loss
Recent deep learning Text-to-Speech (TTS) systems have achieved impressive performance by generating speech close to human parity.
MMLatch: Bottom-up Top-down Fusion for Multimodal Sentiment Analysis
Current deep learning approaches for multimodal fusion rely on bottom-up fusion of high and mid-level latent modality representations (late/mid fusion) or low level sensory inputs (early fusion).
Ranked #7 on
Multimodal Sentiment Analysis
on CMU-MOSEI
(using extra training data)
EmpBot: A T5-based Empathetic Chatbot focusing on Sentiments
Specifically, during finetuning we propose to use three objectives: response language modeling, sentiment understanding, and empathy forcing.
ADMM-DAD net: a deep unfolding network for analysis compressed sensing
In this paper, we propose a new deep unfolding neural network based on the ADMM algorithm for analysis Compressed Sensing.
Unsupervised low-rank representations for speech emotion recognition
We examine the use of linear and non-linear dimensionality reduction algorithms for extracting low-rank feature representations for speech emotion recognition.
UDALM: Unsupervised Domain Adaptation through Language Modeling
In this work we explore Unsupervised Domain Adaptation (UDA) of pretrained language models for downstream tasks.
Ranked #1 on
Sentiment Analysis
on Multi-Domain Sentiment Dataset
Multimodal and Multiresolution Speech Recognition with Transformers
We particularly focus on the scene context provided by the visual information, to ground the ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multiresolution and Multimodal Speech Recognition with Transformers
We particularly focus on the scene context provided by the visual information, to ground the ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Integrating Recurrence Dynamics for Speech Emotion Recognition
We investigate the performance of features that can capture nonlinear recurrence dynamics embedded in the speech signal for the task of Speech Emotion Recognition (SER).
Ranked #46 on
Emotion Recognition in Conversation
on IEMOCAP
Emotion Recognition in Conversation
Speech Emotion Recognition
Pattern Search Multidimensional Scaling
We present a novel view of nonlinear manifold learning using derivative-free optimization techniques.
NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNs
In this paper we present two deep-learning systems that competed at SemEval-2018 Task 3 "Irony detection in English tweets".
NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning
In this paper we present deep-learning models that submitted to the SemEval-2018 Task~1 competition: "Affect in Tweets".
NTUA-SLP at SemEval-2018 Task 2: Predicting Emojis using RNNs with Context-aware Attention
In this paper we present a deep-learning model that competed at SemEval-2018 Task 2 "Multilingual Emoji Prediction".
