755 citations · 1.4k across the 24 of their papers we have counts for
11 papers · 1 filter
Towards Quantifying Benchmark Optimization in ASR Models
Theo Lebryk, David Ayllon, Alice Baird +3
Public benchmarks are important measures of Automatic Speech Recognition (ASR) model capabilities. However, by nature of being public, there is risk of models being optimized for t…
RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems
David Ayllon, Alice Baird, Jeffrey Brooks +11
Current voice AI benchmarks typically evaluate isolated capabilities such as speech intelligibility, word error rate, or text-based dialogue quality, but they rarely test whether s…
TADA: A Generative Framework for Speech Modeling via Text-Acoustic Dual Alignment
Trung Dang, Sharath Rao, Ananya Gupta +6
Modern Text-to-Speech (TTS) systems increasingly leverage Large Language Model (LLM) architectures to achieve scalable, high-fidelity, zero-shot generation. However, these systems…
The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data
Alice Baird, Rachel Manzelli, Panagiotis Tzirakis +7
The NeurIPS 2023 Machine Learning for Audio Workshop brings together machine learning (ML) experts from various audio domains. There are several valuable audio-driven ML tasks, fro…
The ACM Multimedia 2023 Computational Paralinguistics Challenge: Emotion Share & Requests
Björn W. Schuller, Anton Batliner, Shahin Amiriparian +10
The ACM Multimedia 2023 Computational Paralinguistics Challenge addresses two different problems for the first time in a research competition under well-defined conditions: In the…
An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era
Andreas Triantafyllopoulos, Björn W. Schuller, Gökçe İymen +9
Speech is the fundamental mode of human communication, and its synthesis has long been a core priority in human-computer interaction research. In recent years, machines have manage…
A Summary of the ComParE COVID-19 Challenges
Harry Coppock, Alican Akman, Christian Bergler +17
The COVID-19 pandemic has caused massive humanitarian and economic damage. Teams of scientists from a broad range of disciplines have searched for methods to help governments and c…
Evaluating the COVID-19 Identification ResNet (CIdeR) on the INTERSPEECH COVID-19 from Audio Challenges
Alican Akman, Harry Coppock, Alexander Gaskell +3
We report on cross-running the recent COVID-19 Identification ResNet (CIdeR) on the two Interspeech 2021 COVID-19 diagnosis from cough and speech audio challenges: ComParE and DiCO…
Speech Emotion Recognition using Semantic Information
Panagiotis Tzirakis, Anh Nguyen, Stefanos Zafeiriou +1
Speech emotion recognition is a crucial problem manifesting in a multitude of applications such as human computer interaction and education. Although several advancements have been…
Multi-Channel Speech Enhancement using Graph Neural Networks
Panagiotis Tzirakis, Anurag Kumar, Jacob Donley
Multi-channel speech enhancement aims to extract clean speech from a noisy mixture using signals captured from multiple microphones. Recently proposed methods tackle this problem b…
End-2-End COVID-19 Detection from Breath & Cough Audio
Harry Coppock, Alexander Gaskell, Panagiotis Tzirakis +3
Our main contributions are as follows: (I) We demonstrate the first attempt to diagnose COVID-19 using end-to-end deep learning from a crowd-sourced dataset of audio samples, achie…