To extend the SALMON benchmark for evaluating Speech Language Models (SLMs) on more complex acoustic phenomena, several strategies can be implemented. First, the benchmark could incorporate datasets that feature overlapping speech, where multiple speakers talk simultaneously. This would require the development of new evaluation tasks that assess the model's ability to discern and process individual speaker identities and sentiments in a mixed audio environment. Techniques such as source separation algorithms could be employed to isolate speakers, allowing the SLM to evaluate its performance in distinguishing between overlapping voices.
Additionally, dynamic changes in acoustic properties, such as varying background noise levels or room acoustics during a single utterance, could be simulated. This could involve creating audio samples where background noise transitions from one type to another or where the room impulse response changes mid-sentence. By introducing these complexities, the SALMON benchmark would provide a more comprehensive evaluation of an SLM's robustness and adaptability to real-world scenarios, ultimately enhancing its practical applicability in diverse environments.