Publications by Hannah Muckenhirn

Trustworthy speaker recognition with minimal prior knowledge using neural networks

The performance of speaker recognition systems has considerably improved in the last decade. This is mainly due to the development of Gaussian mixture model-based systems and in particular to the use of i-vectors. These systems handle relatively well noise ...

EPFL2019

On Learning to Identify Genders from Raw Speech Signal Using CNNs

Hannah Muckenhirn, Selen Hande Kabil

Automatic Gender Recognition (AGR) is the task of identifying the gender of a speaker given a speech signal. Standard approaches extract features like fundamental frequency and cepstral features from the speech signal and train a binary classifier. Inspire ...

ISCA-INT SPEECH COMMUNICATION ASSOC2018

Towards directly modeling raw speech signal for speaker verification using CNNs

Sébastien Marcel, Hannah Muckenhirn

Speaker verification systems traditionally extract and model cepstral features or filter bank energies from the speech signal. In this paper, inspired by the success of neural network-based approaches to model directly raw speech signal for applications su ...

IEEE2018

On Learning Vocal Tract System Related Speaker Discriminative Information from Raw Signal Using CNNs

Sébastien Marcel, Hannah Muckenhirn

In a recent work, we have shown that speaker verification systems can be built where both features and classifiers are directly learned from the raw speech signal with convolutional neural networks (CNNs). In this framework, the training phase also decides ...

ISCA-INT SPEECH COMMUNICATION ASSOC2018

Long-Term Spectral Statistics for Voice Presentation Attack Detection

Mathew Magimai Doss, Sébastien Marcel, Hannah Muckenhirn

Automatic speaker verification systems can be spoofed through recorded, synthetic or voice converted speech of target speakers. To make these systems practically viable, the detection of such attacks, referred to as presentation attacks, is of paramount in ...

2017

Presentation Attack Detection Using Long-Term Spectral Statistics for Trustworthy Speaker Verification

Mathew Magimai Doss, Sébastien Marcel, Hannah Muckenhirn

In recent years, there has been a growing interest in developing countermeasures against non zero-effort attacks for speaker verification systems. Until now, the focus has been on logical access attacks, where the spoofed samples are injected into the syst ...

Ieee2016