35 citations · 174 across the 23 of their papers we have counts for
25 papers
DialFork: Going Beyond Factuality to Evaluate Procedural Hallucination in Conversational Agents
Sergio Burdisso, Esaú Villatoro-Tello, Dairazalia Sánchez-Cortés +4
Large language models (LLMs) deployed as conversational agents are increasingly expected to follow procedural logic. While factual hallucination has received extensive attention, a…
In-Ear-Voice: Towards Milli-Watt Audio Enhancement With Bone-Conduction Microphones for In-Ear Sensing Platforms
Philipp Schilk, Niccolò Polvani, Andrea Ronco +2
The recent ubiquitous adoption of remote conferencing has been accompanied by omnipresent frustration with distorted or otherwise unclear voice communication. Audio enhancement can…
Speaker Embeddings as Individuality Proxy for Voice Stress Detection
Zihan Wu, Neil Scheidwasser-Clow, Karl El Hajal +1
Since the mental states of the speaker modulate speech, stress introduced by cognitive or physical loads could be detected in the voice. The existing voice stress detection benchma…
ALO-VC: Any-to-any Low-latency One-shot Voice Conversion
Bohan Wang, Damien Ronssin, Milos Cernak
This paper presents ALO-VC, a non-parallel low-latency one-shot phonetic posteriorgrams (PPGs) based voice conversion method. ALO-VC enables any-to-any voice conversion using only…
Personalized Task Load Prediction in Speech Communication
Robert P. Spang, Karl El Hajal, Sebastian Möller +1
Estimating the quality of remote speech communication is a complex task influenced by the speaker, transmission channel, and listener. For example, the degradation of transmission…
BC-VAD: A Robust Bone Conduction Voice Activity Detection
Niccolo' Polvani, Damien Ronssin, Milos Cernak
Voice Activity Detection (VAD) is a fundamental module in many audio applications. Recent state-of-the-art VAD systems are often based on neural networks, but they require a comput…
Efficient Speech Quality Assessment using Self-supervised Framewise Embeddings
Karl El Hajal, Zihan Wu, Neil Scheidwasser-Clow +2
Automatic speech quality assessment is essential for audio researchers, developers, speech and language pathologists, and system quality engineers. The current state-of-the-art sys…
BYOL-S: Learning Self-supervised Speech Representations by Bootstrapping
Gasser Elbanna, Neil Scheidwasser-Clow, Mikolaj Kegler +3
Methods for extracting audio and speech features have been studied since pioneering work on spectrum analysis decades ago. Recent efforts are guided by the ambition to develop gene…
MOSRA: Joint Mean Opinion Score and Room Acoustics Speech Quality Assessment
Karl El Hajal, Milos Cernak, Pablo Mainar
The acoustic environment can degrade speech quality during communication (e.g., video call, remote presentation, outside voice recording), and its impact is often unknown. Objectiv…
Hybrid Handcrafted and Learnable Audio Representation for Analysis of Speech Under Cognitive and Physical Load
Gasser Elbanna, Alice Biryukov, Neil Scheidwasser-Clow +5
As a neurophysiological response to threat or adverse conditions, stress can affect cognition, emotion and behaviour with potentially detrimental effects on health in the case of s…
AC-VC: Non-parallel Low Latency Phonetic Posteriorgrams Based Voice Conversion
Damien Ronssin, Milos Cernak
This paper presents AC-VC (Almost Causal Voice Conversion), a phonetic posteriorgrams based voice conversion system that can perform any-to-many voice conversion while having only…
SERAB: A multi-lingual benchmark for speech emotion recognition
Neil Scheidwasser-Clow, Mikolaj Kegler, Pierre Beckmann +1
Recent developments in speech emotion recognition (SER) often leverage deep neural networks (DNNs). Comparing and benchmarking different DNN models can often be tedious due to the…
PEAF: Learnable Power Efficient Analog Acoustic Features for Audio Recognition
Boris Bergsma, Minhao Yang, Milos Cernak
At the end of Moore's law, new computing paradigms are required to prolong the battery life of wearable and IoT smart audio devices. Theoretical analysis and physical validation ha…
A Universal Deep Room Acoustics Estimator
Paula Sánchez López, Paul Callens, Milos Cernak
Speech audio quality is subject to degradation caused by an acoustic environment and isotropic ambient and point noises. The environment can lead to decreased speech intelligibilit…
Joint Blind Room Acoustic Characterization From Speech And Music Signals Using Convolutional Recurrent Neural Networks
Paul Callens, Milos Cernak
Acoustic environment characterization opens doors for sound reproduction innovations, smart EQing, speech enhancement, hearing aids, and forensics. Reverberation time, clarity, and…
FastVC: Fast Voice Conversion with non-parallel data
Oriol Barbany Mayor, Milos Cernak
This paper introduces FastVC, an end-to-end model for fast Voice Conversion (VC). The proposed model can convert speech of arbitrary length from multiple source speakers to multipl…
Fast accuracy estimation of deep learning based multi-class musical source separation
Alexandru Mocanu, Benjamin Ricaud, Milos Cernak
Music source separation represents the task of extracting all the instruments from a given song. Recent breakthroughs on this challenge have gravitated around a single dataset, MUS…
A Bin Encoding Training of a Spiking Neural Network-based Voice Activity Detection
Giorgia Dellaferrera, Flavio Martinelli, Milos Cernak
Advances of deep learning for Artificial Neural Networks(ANNs) have led to significant improvements in the performance of digital signal processing systems implemented on digital c…
Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection
Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar +1
Recent advances in Voice Activity Detection (VAD) are driven by artificial and Recurrent Neural Networks (RNNs), however, using a VAD system in battery-operated devices requires fu…
Word-level Embeddings for Cross-Task Transfer Learning in Speech Processing
Pierre Beckmann, Mikolaj Kegler, Milos Cernak
Recent breakthroughs in deep learning often rely on representation learning and knowledge transfer. In recent years, unsupervised and self-supervised techniques for learning speech…
Deep speech inpainting of time-frequency masks
Mikolaj Kegler, Pierre Beckmann, Milos Cernak
Transient loud intrusions, often occurring in noisy environments, can completely overpower speech signal and lead to an inevitable loss of information. While existing algorithms fo…
Composition of Deep and Spiking Neural Networks for Very Low Bit Rate Speech Coding
Milos Cernak, Alexandros Lazaridis, Afsaneh Asaei +1
Most current very low bit rate (VLBR) speech coding systems use hidden Markov model (HMM) based speech recognition/synthesis techniques. This allows transmission of information (su…
An Analysis of Rhythmic Staccato-Vocalization Based on Frequency Demodulation for Laughter Detection in Conversational Meetings
Sucheta Ghosh, Milos Cernak, Sarbani Palit +1
Human laugh is able to convey various kinds of meanings in human communications. There exists various kinds of human laugh signal, for example: vocalized laugh and non vocalized la…
Speech vocoding for laboratory phonology
Milos Cernak, Stefan Benus, Alexandros Lazaridis
Using phonological speech vocoding, we propose a platform for exploring relations between phonology and speech processing, and in broader terms, for exploring relations between the…