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

1 code implementation • 23 May 2023 • Eklavya Sarkar, Mathew Magimai. -Doss

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.

Caller Detection Self-Supervised Learning +1

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

1 code implementation • 27 Jun 2022 • Eklavya Sarkar, RaviShankar Prasad, Mathew Magimai. -Doss

This paper investigates the potential of zero-frequency filtering for jointly modeling voice source and vocal tract system information, and proposes two approaches for VAD.

Action Detection Activity Detection

An Objective Evaluation Framework for Pathological Speech Synthesis

no code implementations • 1 Jul 2021 • Bence Mark Halpern, Julian Fritsch, Enno Hermann, Rob van Son, Odette Scharenborg, Mathew Magimai. -Doss

The development of pathological speech systems is currently hindered by the lack of a standardised objective evaluation framework.

Speech Synthesis Voice Conversion

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

no code implementations • LREC 2020 • S Tornay, rine, Oya Aran, Mathew Magimai. -Doss

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.

Gesture Recognition Model Selection

Learning linearly separable features for speech recognition using convolutional neural networks

no code implementations • 22 Dec 2014 • Dimitri Palaz, Mathew Magimai. -Doss, Ronan Collobert

This system was shown to yield similar or better performance than HMM/ANN based system on phoneme recognition task and on large scale continuous speech recognition task, using less parameters.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

End-to-end Phoneme Sequence Recognition using Convolutional Neural Networks

1 code implementation • 7 Dec 2013 • Dimitri Palaz, Ronan Collobert, Mathew Magimai. -Doss

Most phoneme recognition state-of-the-art systems rely on a classical neural network classifiers, fed with highly tuned features, such as MFCC or PLP features.

Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks

no code implementations • 3 Apr 2013 • Dimitri Palaz, Ronan Collobert, Mathew Magimai. -Doss

Motivated from these studies, in the framework of convolutional neural networks (CNNs), this paper investigates a novel approach, where the input to the ANN is raw speech signal and the output is phoneme class conditional probability estimates.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1