Year: 2025
Type: conference-paper
Abstract: Speech language models have recently demonstrated great potential as universal speech processing systems. Such models have the ability to model the rich acoustic information existing in audio signals, beyond spoken content, such as emotion, background noise, etc. Despite this, evaluation benchmarks which evaluate awareness to a wide range of acoustic aspects, are lacking. To help bridge this gap, we introduce SALMON, a novel evaluation suite encompassing background noise, sentiment, speaker identity and room impulse response. The proposed benchmarks both evaluate the consistency of the inspected element and how much it matches the spoken text. We follow a modelling based approach, measuring whether a model gives correct samples higher scores than incorrect ones. This approach makes the benchmark fast to compute even for large models. We evaluated several speech language models on SALMON, thus highlighting the strengths and weaknesses of each evaluated method. We make the code and data publicly available at - pages.cs.huji.ac.il/adiyoss-lab/salmon/.
Authors: Gallil Maimon, Amit Roth, Yossi Adi
Institution: Hebrew University of Jerusalem
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Israel Science Foundation
OpenAlex ID: W4408352488