Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.25460 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912983798513664 |
|---|---|
| author | Huang, Shangkun Shen, Huan Zou, Wei Chen, Yunzhang |
| author_facet | Huang, Shangkun Shen, Huan Zou, Wei Chen, Yunzhang |
| contents | Speech LLM-based ASR often struggles with named entities and long-tail words due to strong internal language-model priors. Retrieval-augmented biasing can help, but its effectiveness depends on accurate hotword localization in full-utterance speech under weak supervision. We propose CLAR, a dual-encoder speech-text retriever that uses Continuous Integrate-and-Fire (CIF) to learn monotonic token-level alignments without timestamps. With length-aware localized matching, CLAR anchors short-entity acoustic cues and reduces representation dilution and attention drift. The retriever is trained with a multi-granularity objective combining global and local segment-level contrastive losses and a CIF quantity constraint. At inference, top-ranked hotwords are injected as contextual prompts for the Speech LLM, improving recognition without shallow fusion. Experiments show that CLAR significantly improves hotword retrieval and reduces both CER and B-WER against strong contextual ASR baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_25460 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CLAR: CIF-Localized Alignment for Retrieval-Augmented Speech LLM-Based Contextual ASR Huang, Shangkun Shen, Huan Zou, Wei Chen, Yunzhang Sound Speech LLM-based ASR often struggles with named entities and long-tail words due to strong internal language-model priors. Retrieval-augmented biasing can help, but its effectiveness depends on accurate hotword localization in full-utterance speech under weak supervision. We propose CLAR, a dual-encoder speech-text retriever that uses Continuous Integrate-and-Fire (CIF) to learn monotonic token-level alignments without timestamps. With length-aware localized matching, CLAR anchors short-entity acoustic cues and reduces representation dilution and attention drift. The retriever is trained with a multi-granularity objective combining global and local segment-level contrastive losses and a CIF quantity constraint. At inference, top-ranked hotwords are injected as contextual prompts for the Speech LLM, improving recognition without shallow fusion. Experiments show that CLAR significantly improves hotword retrieval and reduces both CER and B-WER against strong contextual ASR baselines. |
| title | CLAR: CIF-Localized Alignment for Retrieval-Augmented Speech LLM-Based Contextual ASR |
| topic | Sound |
| url | https://arxiv.org/abs/2603.25460 |