21 citations · 81 across the 18 of their papers we have counts for
19 papers
Investigating Personalization Methods in Text to Music Generation
Manos Plitsis, Theodoros Kouzelis, Georgios Paraskevopoulos +2
In this work, we investigate the personalization of text-to-music diffusion models in a few-shot setting. Motivated by recent advances in the computer vision domain, we are the fir…
Weakly-supervised forced alignment of disfluent speech using phoneme-level modeling
Theodoros Kouzelis, Georgios Paraskevopoulos, Athanasios Katsamanis +1
The study of speech disorders can benefit greatly from time-aligned data. However, audio-text mismatches in disfluent speech cause rapid performance degradation for modern speech a…
Depression detection in social media posts using affective and social norm features
Ilias Triantafyllopoulos, Georgios Paraskevopoulos, Alexandros Potamianos
We propose a deep architecture for depression detection from social media posts. The proposed architecture builds upon BERT to extract language representations from social media po…
Sample-Efficient Unsupervised Domain Adaptation of Speech Recognition Systems A case study for Modern Greek
Georgios Paraskevopoulos, Theodoros Kouzelis, Georgios Rouvalis +3
Modern speech recognition systems exhibits rapid performance degradation under domain shift. This issue is especially prevalent in data-scarce settings, such as low-resource langua…
Adapted Multimodal BERT with Layer-wise Fusion for Sentiment Analysis
Odysseas S. Chlapanis, Georgios Paraskevopoulos, Alexandros Potamianos
Multimodal learning pipelines have benefited from the success of pretrained language models. However, this comes at the cost of increased model parameters. In this work, we propose…
Extending Compositional Attention Networks for Social Reasoning in Videos
Christina Sartzetaki, Georgios Paraskevopoulos, Alexandros Potamianos
We propose a novel deep architecture for the task of reasoning about social interactions in videos. We leverage the multi-step reasoning capabilities of Compositional Attention Net…
Regotron: Regularizing the Tacotron2 architecture via monotonic alignment loss
Efthymios Georgiou, Kosmas Kritsis, Georgios Paraskevopoulos +3
Recent deep learning Text-to-Speech (TTS) systems have achieved impressive performance by generating speech close to human parity. However, they suffer from training stability issu…
MMLatch: Bottom-up Top-down Fusion for Multimodal Sentiment Analysis
Georgios Paraskevopoulos, Efthymios Georgiou, Alexandros Potamianos
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…
EmpBot: A T5-based Empathetic Chatbot focusing on Sentiments
Emmanouil Zaranis, Georgios Paraskevopoulos, Athanasios Katsamanis +1
In this paper, we introduce EmpBot: an end-to-end empathetic chatbot. Empathetic conversational agents should not only understand what is being discussed, but also acknowledge the…
ADMM-DAD net: a deep unfolding network for analysis compressed sensing
Vasiliki Kouni, Georgios Paraskevopoulos, Holger Rauhut +1
In this paper, we propose a new deep unfolding neural network based on the ADMM algorithm for analysis Compressed Sensing. The proposed network jointly learns a redundant analysis…
UDALM: Unsupervised Domain Adaptation through Language Modeling
Constantinos Karouzos, Georgios Paraskevopoulos, Alexandros Potamianos
In this work we explore Unsupervised Domain Adaptation (UDA) of pretrained language models for downstream tasks. We introduce UDALM, a fine-tuning procedure, using a mixed classifi…
Unsupervised low-rank representations for speech emotion recognition
Georgios Paraskevopoulos, Efthymios Tzinis, Nikolaos Ellinas +2
We examine the use of linear and non-linear dimensionality reduction algorithms for extracting low-rank feature representations for speech emotion recognition. Two feature sets are…
Affective Conditioning on Hierarchical Networks applied to Depression Detection from Transcribed Clinical Interviews
D. Xezonaki, G. Paraskevopoulos, A. Potamianos +1
In this work we propose a machine learning model for depression detection from transcribed clinical interviews. Depression is a mental disorder that impacts not only the subject's…
Multiresolution and Multimodal Speech Recognition with Transformers
Georgios Paraskevopoulos, Srinivas Parthasarathy, Aparna Khare +1
This paper presents an audio visual automatic speech recognition (AV-ASR) system using a Transformer-based architecture. We particularly focus on the scene context provided by the…
Integrating Recurrence Dynamics for Speech Emotion Recognition
Efthymios Tzinis, Georgios Paraskevopoulos, Christos Baziotis +1
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). Reconstruc…
Pattern Search Multidimensional Scaling
Georgios Paraskevopoulos, Efthymios Tzinis, Emmanouil-Vasileios Vlatakis-Gkaragkounis +1
We present a novel view of nonlinear manifold learning using derivative-free optimization techniques. Specifically, we propose an extension of the classical multi-dimensional scali…
NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNs
Christos Baziotis, Nikos Athanasiou, Pinelopi Papalampidi +4
In this paper we present two deep-learning systems that competed at SemEval-2018 Task 3 "Irony detection in English tweets". We design and ensemble two independent models, based on…
NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning
Christos Baziotis, Nikos Athanasiou, Alexandra Chronopoulou +5
In this paper we present deep-learning models that submitted to the SemEval-2018 Task~1 competition: "Affect in Tweets". We participated in all subtasks for English tweets. We prop…
NTUA-SLP at SemEval-2018 Task 2: Predicting Emojis using RNNs with Context-aware Attention
Christos Baziotis, Nikos Athanasiou, Georgios Paraskevopoulos +3
In this paper we present a deep-learning model that competed at SemEval-2018 Task 2 "Multilingual Emoji Prediction". We participated in subtask A, in which we are called to predict…