Publications de Mathew Magimai Doss

On the Utility of Speech and Audio Foundation Models for Marmoset Call Analysis

Marmoset monkeys encode vital information in their calls and serve as a surrogate model for neuro-biologists to understand the evolutionary origins of human vocal communication. Traditionally analyzed with signal processing-based features, recent approache ...

2024

Predicting Heart Activity from Speech using Data-driven and Knowledge-based features

Mathew Magimai Doss, Zohreh Mostaani

Accurately predicting heart activity and other biological signals is crucial for diagnosis and monitoring. Given that speech is an outcome of multiple physiological systems, a significant body of work studied the acoustic correlates of heart activity. Rece ...

ISCA Archives2024

Feature Representations for Automatic Meerkat Vocalization Classification

Mathew Magimai Doss, Eklavya Sarkar

Understanding evolution of vocal communication in social animals is an important research problem. In that context, beyond humans, there is an interest in analyzing vocalizations of other social animals such as, meerkats, marmosets, apes. While existing ap ...

2024

Can Self-Supervised Neural Representations Pre-Trained on Human Speech distinguish Animal Callers?

Mathew Magimai Doss, Eklavya Sarkar

Self-supervised learning (SSL) models use only the intrinsic structure of a given signal, independent of its acoustic domain, to extract essential information from the input to an embedding space. This implies that the utility of such representations is no ...

ISCA2023

On Breathing Pattern Information in Synthetic Speech

Mathew Magimai Doss, Zohreh Mostaani

The respiratory system is an integral part of human speech production. As a consequence, there is a close relation between respiration and speech signal, and the produced speech signal carries breathing pattern related information. Speech can also be gener ...

ISCA-INT SPEECH COMMUNICATION ASSOC2022

Adjustable deterministic pseudonymization of speech

Mathew Magimai Doss, Subrahmanya Pavankumar Dubagunta

While public speech resources become increasingly available, there is a growing interest to preserve the privacy of the speakers, through methods that anonymize the speaker information from speech while preserving the spoken linguistic content. In this pap ...

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD2022

Unsupervised Voice Activity Detection by Modeling Source and System Information using Zero Frequency Filtering

Mathew Magimai Doss, Eklavya Sarkar

Voice activity detection (VAD) is an important pre-processing step for speech technology applications. The task consists of deriving segment boundaries of audio signals which contain voicing information. In recent years, it has been shown that voice source ...

ISCA2022

On The Relationship Between Speech-Based Breathing Signal Prediction Evaluation Measures And Breathing Parameters Estimation

Mathew Magimai Doss, Zohreh Mostaani, Venkata Srikanth Nallanthighal

The respiratory system is one of the major components of the speech production system. Any alteration in breathing can result in changes in speech. Specific breathing characteristics, such as breathing rate and tidal volume, can indicate a person's patholo ...

IEEE2021

Late Fusion of the Available Lexicon and Raw Waveform-based Acoustic Modeling for Depression and Dementia Recognition

Mathew Magimai Doss, Petr Motlicek, Subrahmanya Pavankumar Dubagunta, Julian David Fritsch

Mental disorders, e.g. depression and dementia, are categorized as priority conditions according to the World Health Organization (WHO). When diagnosing, psychologists employ structured questionnaires/interviews, and different cognitive tests. Although acc ...

ISCA-INT SPEECH COMMUNICATION ASSOC2021

Deep learning architectures for estimating breathing signal and respiratory parameters from speech recordings

Mathew Magimai Doss, Zohreh Mostaani, Venkata Srikanth Nallanthighal

Respiration is an essential and primary mechanism for speech production. We first inhale and then produce speech while exhaling. When we run out of breath, we stop speaking and inhale. Though this process is involuntary, speech production involves a system ...

PERGAMON-ELSEVIER SCIENCE LTD2021

Utterance Verification-Based Dysarthric Speech Intelligibility Assessment Using Phonetic Posterior Features

Mathew Magimai Doss, Julian David Fritsch

In the literature, the task of dysarthric speech intelligibility assessment has been approached through development of different low-level feature representations, subspace modeling, phone confidence estimation or measurement of automatic speech recognitio ...

IEEE Institute of Electrical and Electronics Engineers2021

On Modeling Glottal Source Information for Phonation Assessment in Parkinson's Disease

Mathew Magimai Doss, Julian David Fritsch

Parkinson's disease produces several motor symptoms, including different speech impairments that are known as hypokinetic dysarthria. Symptoms associated to dysarthria affect different dimensions of speech such as phonation, articulation, prosody, and inte ...

ISCA-INT SPEECH COMMUNICATION ASSOC2021

Signal-to-signal neural networks for improved spike estimation from calcium imaging data

Mathew Magimai Doss, Jilt Sebastian

Information processing by a population of neurons is studied using two-photon calcium imaging techniques. A neuronal spike results in an increased intracellular calcium concentration. Fluorescent calcium indicators change their brightness upon a change in ...

2021

On Joint Optimization of Automatic Speaker Verification and Anti-spoofing in the Embedding Space

Mathew Magimai Doss, Subrahmanya Pavankumar Dubagunta

IEEE2021

A Phonology-based Approach for Isolated Sign Production Assessment in Sign Language

Mathew Magimai Doss, Necati Cihan Camgöz, Sandrine Tornay

ACM2020

An HMM Approach with Inherent Model Selection for Sign Language and Gesture Recognition

Mathew Magimai Doss, Oya Aran, Sandrine Tornay

HMMs have been the one of the first models to be applied for sign recognition and have become the baseline models due to their success in modeling sequential and multivariate data. Despite the extensive use of HMMs for sign recognition, determining the HMM ...

EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA2020

A Comparison of Acoustic and Linguistics Methodologies for Alzheimer's Dementia Recognition

Mathew Magimai Doss, Venkata Srikanth Nallanthighal, Julian David Fritsch

ISCA2020

End-to-End Acoustic Modeling using Convolutional Neural Networks for HMM-based Automatic Speech Recognition

Mathew Magimai Doss, Dimitri Palaz, Ronan Collobert

In hidden Markov model (HMM) based automatic speech recognition (ASR) system, modeling the statistical relationship between the acoustic speech signal and the HMM states that represent linguistically motivated subword units such as phonemes is a crucial st ...

ELSEVIER SCIENCE BV2019

Denoising and Raw-waveform Networks for Weakly-Supervised Gender Identification on Noisy Speech

Mathew Magimai Doss, Manoj Kumar, Jilt Sebastian

This paper presents a raw-waveform neural network and uses it along with a denoising network for clustering in weakly supervised learning scenarios under extreme noise conditions. Specifically, we consider language independent Automatic Gender Recognition ...

ISCA-INT SPEECH COMMUNICATION ASSOC2018

Towards Weakly Supervised Acoustic Subword Unit Discovery and Lexicon Development Using Hidden Markov Models

Mathew Magimai Doss, Ramya Rasipuram, Marzieh Razavi

2018