Athanasios Katsamanis

National Technical University of Athens

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Co-Authors

Petros Maragos

National Technical University of Athens

Shrikanth Narayanan

University of Southern California

Panayiotis Georgiou

Apple (United Kingdom)

Vassilis Pitsikalis

National Technical University of Athens

George Papandreou

Aristotle University of Thessaloniki

Matthew Black

University of Siegen

Antigoni Tsiami

National Technical University of Athens

Chi-Chun Lee

National Tsing Hua University

Gerasimos Potamianos

University of Thessaly

Alexandros Potamianos

National Technical University of Athens

Cooperative Institutions

University of Southern California

Southern California University for Professional Studies

National Technical University of Athens

National and Kapodistrian University of Athens

Institute for Language and Speech Processing

Athena Research and Innovation Center In Information Communication & Knowledge Technologies

Google (United States)

University of California, Los Angeles

Signal Processing (United States)

Creative Technologies (United States)

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Direct estimation of articulatory kinematics from real-time magnetic resonance image sequences

Interspeech 2022 (2011)

Michael Proctor Adam Lammert Athanasios Katsamanis Louis Goldstein Christina Hagedorn

A method of rapid, automatic extraction of consonantal articulatory trajectories from real-time magnetic resonance image sequences is described. Constriction location targets are estimated by identifying regions of maximally-dynamic correlated pixel activity along the palate, the alveolar ridge, and at the lips. Tissue movement into and out of the constriction location is estimated by calculating the change in mean pixel intensity in a circle located at the center of the region of interest. Closure and release gesture timings are estimated from landmarks in the velocity profile derived from the smoothed intensity function. We demonstrate the utility of the technique in the analysis of Italian intervocalic consonant production. Index Terms: speech production, real-time MRI, consonant articulation, tongue shaping, articulatory phonology

Articulation (sociology)

Intensity

Region of interest

10.21437/interspeech.2011-110

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Citations (26)

Sample-Efficient Unsupervised Domain Adaptation of Speech Recognition Systems: A case study for Modern Greek

Georgios Paraskevopoulos Theodoros Kouzelis Georgios Rouvalis Athanasios Katsamanis Vassilis Katsouros

<p>Modern speech recognition systems exhibits rapid performance degradation under domain shift. This issue is especially prevalent in data-scarce settings, such as low-resource languages, where diversity of training data is limited.</p> <p>In this work we propose M2DS2, a simple and sample-efficient finetuning strategy for large pretrained speech models, based on mixed source and target domain self-supervision. We find that including source domain self-supervision stabilizes training and avoids mode collapse of the latent representations. For evaluation, we collect HParl, a 120 hour speech corpus for Greek, consisting of plenary sessions in the Greek Parliament. We merge HParl with two popular Greek corpora to create GREC-MD, a test-bed for multi-domain evaluation of Greek ASR systems. In our experiments we find that, while other Unsupervised Domain Adaptation baselines fail in this resource-constrained environment, M2DS2 yields significant improvements for cross-domain adaptation, even when a only a few hours of in-domain audio are available. When we relax the problem in a weakly supervised setting, we find that independent adaptation for audio using M2DS2 and language using simple LM augmentation techniques is particularly effective, yielding word error rates comparable to the fully supervised baselines.</p>

Merge (version control)

Domain Adaptation

Greek language

Sample (material)

10.36227/techrxiv.21792920.v1

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Cross-Lingual Features for Alzheimer’s Dementia Detection from Speech

Interspeech 2022 (2023)

Θωμάς Μελίστας Lefteris Kapelonis Nikos Antoniou Petros Mitseas Dimitris Sgouropoulos

10.21437/interspeech.2023-1934

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Based on isolated saliency or causal integration? toward a better understanding of human annotation process using multiple instance learning and sequential probability ratio test

Interspeech 2022 (2012)

Chi-Chun Lee Athanasios Katsamanis Panayiotis Georgiou Shrikanth Narayanan

Human perception is capable of integrating local events to generate an overall impression at the global level; this is evident in daily life and is utilized repeatedly in behavioral science studies to bring objective measures into studies of human behavior. In this work, we explore two hypotheses considering whether it is the isolated-saliency or the causal-integration of information that can trigger the global perceptual behavioral ratings as trained annotators engage in tasks of observational coding. We carry out analyses using Multiple Instance Learning and Sequential Probability Ratio Test in a corpus of real and spontaneous distressed couples’ interaction with global sessionlevel abstract behavioral coding done by trained human annotators. We present various analyses based on different behavioral detection schemes demonstrating the potential of utilizing these algorithms in bringing insights into the human annotation process. We further show that while annotating behaviors with more positive impression, annotators gather information throughout the session compared to behaviors with more negative impression, where a single salient instance is enough to trigger the final global decision.

Impression

10.21437/interspeech.2012-187

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Citations (12)

Advances in statistical estimation and tracking of AM-FM speech components

Interspeech 2022 (2005)

Athanasios Katsamanis Petros Maragos

In this paper we present two extensions of a statistical framework to demodulate speech resonances, which are modeled as AM-FM signals. The first approach utilizes bandpass filtering and a standard demodulation algorithm which regularizes instantaneous amplitude and frequency estimates. The second employs particle filtering techniques to allow temporal variations of the parameters that are connected with spectral characteristics of the analyzed signal. Results are presented on both synthetic and real speech signals and improved performance is demonstrated. Both approaches appear to cope quite satisfactorily with the nonstationarity of speech signals.

SIGNAL (programming language)

Instantaneous phase

10.21437/interspeech.2005-206

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Photorealistic adaptation and interpolation of facial expressions using HMMS and AAMS for audio-visual speech synthesis

2022 IEEE International Conference on Image Processing (ICIP) (2017)

Panagiotis P. Filntisis Athanasios Katsamanis Petros Maragos

In this paper, motivated by the continuously increasing presence of intelligent agents in everyday life, we address the problem of expressive photorealistic audio-visual speech synthesis, with a strong focus on the visual modality. Emotion constitutes one of the main driving factors of social life and it is expressed mainly through facial expressions. Synthesis of a talking head capable of expressive audio-visual speech is challenging due to the data overhead that arises when considering the vast number of emotions we would like the talking head to express. In order to tackle this challenge, we propose the usage of two methods, namely Hidden Markov Model (HMM) adaptation and interpolation, with HMMs modeling visual parameters via an Active Appearance Model (AAM) of the face. We show that through HMM adaptation we can successfully adapt a "neutral" talking head to a target emotion with a small amount of adaptation data, as well as that through HMM interpolation we can robustly achieve different levels of intensity for an emotion.

Interpolation

Active appearance model

10.1109/icip.2017.8296821

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Citations (4)

Predicting audio-visual salient events based on visual, audio and text modalities for movie summarization

2022 IEEE International Conference on Image Processing (ICIP) (2015)

Petros Koutras Athanasia Zlatintsi Elias Iosif Athanasios Katsamanis Petros Maragos

In this paper, we present a new and improved synergistic approach to the problem of audio-visual salient event detection and movie summarization based on visual, audio and text modalities. Spatio-temporal visual saliency is estimated through a perceptually inspired frontend based on 3D (space, time) Gabor filters and frame-wise features are extracted from the saliency volumes. For the auditory salient event detection we extract features based on Teager-Kaiser Energy Operator, while text analysis incorporates part-of-speech tagging and affective modeling of single words on the movie subtitles. For the evaluation of the proposed system, we employ an elementary and non-parametric classification technique like KNN. Detection results are reported on the MovSum database, using objective evaluations against ground-truth denoting the perceptually salient events, and human evaluations of the movie summaries. Our evaluation verifies the appropriateness of the proposed methods compared to our baseline system. Finally, our newly proposed summarization algorithm produces summaries that consist of salient and meaningful events, also improving the comprehension of the semantics.

Audio visual

Modalities

10.1109/icip.2015.7351630

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Citations (26)

Experiments in acoustic source localization using sparse arrays in adverse indoors environments

European Signal Processing Conference (2014)

Antigoni Tsiami Athanasios Katsamanis Petros Maragos Gerasimos Potamianos

In this paper we experiment with 2-D source localization in smart homes under adverse conditions using sparse distributed microphone arrays. We propose some improvements to deal with problems due to high reverberation, noise and use of a limited number of microphones. These consist of a pre-filtering stage for dereverberation and an iterative procedure that aims to increase accuracy. Experiments carried out in relatively large databases with both simulated and real recordings of sources in various positions indicate that the proposed method exhibits a better performance compared to others under challenging conditions while also being computationally efficient. It is demonstrated that although reverberation degrades localization performance, this degradation can be compensated by identifying the reliable microphone pairs and disposing of the outliers.

Degradation

Source

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Citations (6)

Automatic classification of married couples' behavior using audio features

Interspeech 2022 (2010)

Matthew Black Athanasios Katsamanis Chi-Chun Lee Adam Lammert Brian R. Baucom

In this work, we analyzed a 96-hour corpus of married couples spontaneously interacting about a problem in their relationship. Each spouse was manually coded with relevant session-level perceptual observations (e.g., level of blame toward other spouse, global positive affect), and our goal was to classify the spouses’ behavior using features derived from the audio signal. Based on automatic segmentation, we extracted prosodic/spectral features to capture global acoustic properties for each spouse. We then trained gender-specific classifiers to predict the behavior of each spouse for six codes. We compare performance for the various factors (across codes, gender, classifier type, and feature type) and discuss future work for this novel and challenging corpus. Index Terms: behavioral signal processing, human behavior analysis, couples therapy, prosody, emotion recognition

Spouse

Blame

Feature (linguistics)

Affect

10.21437/interspeech.2010-574

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Citations (51)

ATHENA: a Greek multi-sensory database for home automation control uthor: isidoros rodomagoulakis (NTUA, Greece)

Interspeech 2022 (2014)

Antigoni Tsiami Isidoros Rodomagoulakis Panagiotis Giannoulis Athanasios Katsamanis Gerasimos Potamianos

Home Automation

10.21437/interspeech.2014-382

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Citations (3)