SSR: Alignment-Aware Modality Connector for Speech Language Models

• URL: http://arxiv.org/abs/2410.00168v1
• Date: Mon, 30 Sep 2024 19:17:46 GMT
• Title: SSR: Alignment-Aware Modality Connector for Speech Language Models
• Authors: Weiting Tan, Hirofumi Inaguma, Ning Dong, Paden Tomasello, Xutai Ma,
• Abstract summary: 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.
• Score: 23.859649312290447
• License: http://creativecommons.org/licenses/by/4.0/
• 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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