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arXiv · 2410.00168

SSR: Alignment-Aware Modality Connector for Speech Language Models

Weiting Tan·Hirofumi Inaguma·Ning Dong·Paden Tomasello·Xutai Ma
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Abstract

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.

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Weiting Tan, Hirofumi Inaguma, Ning Dong, Paden Tomasello, Xutai Ma. 2024-09-30. SSR: Alignment-Aware Modality Connector for Speech Language Models. https://arxiv.org/abs/2410.00168

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