To improve the alignment between acoustic and semantic information in Speech Language Models (SLMs), several architectural and training approaches can be considered. One promising direction is the integration of multi-modal learning frameworks that jointly process audio and text inputs. By employing architectures that utilize attention mechanisms, such as Transformers, SLMs can learn to correlate acoustic features with their corresponding semantic meanings more effectively. This could involve training on datasets that include both audio recordings and their transcriptions, allowing the model to learn the contextual relationships between sound and meaning.
Another approach is to enhance the model's training with adversarial techniques, where the SLM is challenged to differentiate between real and synthetic audio samples that vary in sentiment or background noise. This could help the model develop a deeper understanding of how acoustic variations influence semantic interpretation. Furthermore, incorporating expressive features, such as pitch and intonation, into the training process can enable the SLM to better capture the nuances of speech that convey emotional and contextual information, leading to improved performance in tasks requiring semantic-acoustic alignment.