SSR: Alignment-Aware Modality Connector for Speech Language Models

Fusing speech into pre-trained language model (SpeechLM) usually suffers from inefficient encoding of long-form speech and catastrophic forgetting of pre-trained text modality. We propose SSR-Connector (Segmented Speech Representation Connector) for better modality fusion. Leveraging speech-text alignments, our approach segments and compresses speech features to match the granularity of text embeddings. Additionally, we introduce a two-stage training pipeline that includes the distillation and fine-tuning phases to mitigate catastrophic forgetting. SSR-Connector outperforms existing mechanism for speech-text modality fusion, consistently achieving better speech understanding (e.g., +10 accuracy on StoryCloze and +20 on Speech-MMLU) while preserving pre-trained text ability.

@article{tan2025_2410.00168,
  title={ SSR: Alignment-Aware Modality Connector for Speech Language Models },
  author={ Weiting Tan and Hirofumi Inaguma and Ning Dong and Paden Tomasello and Xutai Ma },
  journal={arXiv preprint arXiv:2410.00168},
  year={ 2025 }
}