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 SALMonš£, a novel evaluation suite encompassing background noise, emotion, 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 SALMonš£, thus highlighting the strengths and weaknesses of each evaluated method. Code and data are publicly available.
| Method | Sentiment Consistency | Speaker Consistency | Gender Consistency | Background Consistency (In-Domain) | Background Consistency (Random) | Room Consistency | Sentiment Alignment | Background Alignment | sWUGGY |
|---|---|---|---|---|---|---|---|---|---|
| Human Baseline | 97.2 | 91.2 | 98.6 | 83.1 | 88.7 | 94.4 | 93.3 | 95.7 | |
| LAST 1.3B | 65.0 | 64.5 | 68.5 | 56.0 | 61.0 | 62.5 | 53.5 | 53.0 | 73.6 |
| TWIST 7B | 61.5 | 71.0 | 70.0 | 55.0 | 60.5 | 62.0 | 51.5 | 54.5 | 82.8 |
| pGSLM | 40.5 | 83.0 | 88.5 | 57.0 | 66.0 | 53.5 | 55.5 | 53.5 | 74.1 |
| SPIRIT LM | 54.5 | 69.5 | 67.0 | 53.5 | 55.5 | 54.5 | 48.0 | 51.5 | 75.5 |
| SPIRIT LM Expr. | 73.5 | 81.0 | 85.0 | 55.0 | 64.0 | 54.5 | 52.0 | 59.5 | 72.7 |
| Task | Sample | Positive Audio | Negative Audio |
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Speaker Consistency |
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Gender Consistency |
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Background Consistency (Random) |
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Background Consistency (In-Domain) |
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Room Impulse Response Consistency |
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Background Alignment |
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@article{maimon2024salmon,
title={A Suite for Acoustic Language Model Evaluation},
author={Maimon, Gallil and Roth, Amit and Adi, Yossi},
journal={arXiv preprint arXiv:2409.07437},
year={2024}
}