no code implementations • 9 Jun 2023 • Zihan Wu, Neil Scheidwasser-Clow, Karl El Hajal, Milos Cernak
However, the benchmark only evaluates performance separately on each dataset, but does not evaluate performance across the different types of stress and different languages.
no code implementations • 12 Nov 2022 • Karl El Hajal, Zihan Wu, Neil Scheidwasser-Clow, Gasser Elbanna, Milos Cernak
Automatic speech quality assessment is essential for audio researchers, developers, speech and language pathologists, and system quality engineers.
1 code implementation • 24 Jun 2022 • Gasser Elbanna, Neil Scheidwasser-Clow, Mikolaj Kegler, Pierre Beckmann, Karl El Hajal, Milos Cernak
Our results indicate that the hybrid model with a convolutional transformer as the encoder yields superior performance in most HEAR challenge tasks.
Ranked #1 on Self-Supervised Learning on CREMA-D
1 code implementation • 30 Mar 2022 • Gasser Elbanna, Alice Biryukov, Neil Scheidwasser-Clow, Lara Orlandic, Pablo Mainar, Mikolaj Kegler, Pierre Beckmann, Milos Cernak
To that end, we introduce a set of five datasets for task load detection in speech.
2 code implementations • 7 Oct 2021 • Neil Scheidwasser-Clow, Mikolaj Kegler, Pierre Beckmann, Milos Cernak
To facilitate the process, here, we present the Speech Emotion Recognition Adaptation Benchmark (SERAB), a framework for evaluating the performance and generalization capacity of different approaches for utterance-level SER.