TICL+: A Case Study On Speech In-Context Learning for Children's Speech Recognition
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909972344864768 |
|---|---|
| author | Zheng, Haolong Yegorova, Yekaterina Hasegawa-Johnson, Mark |
| author_facet | Zheng, Haolong Yegorova, Yekaterina Hasegawa-Johnson, Mark |
| contents | Children's speech recognition remains challenging due to substantial acoustic and linguistic variability, limited labeled data, and significant differences from adult speech. Speech foundation models can address these challenges through Speech In-Context Learning (SICL), allowing adaptation to new domains without fine-tuning. However, the effectiveness of SICL depends on how in-context examples are selected. We extend an existing retrieval-based method, Text-Embedding KNN for SICL (TICL), introducing an acoustic reranking step to create TICL+. This extension prioritizes examples that are both semantically and acoustically aligned with the test input. Experiments on four children's speech corpora show that TICL+ achieves up to a 53.3% relative word error rate reduction over zero-shot performance and 37.6% over baseline TICL, highlighting the value of combining semantic and acoustic information for robust, scalable ASR in children's speech. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_18263 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TICL+: A Case Study On Speech In-Context Learning for Children's Speech Recognition Zheng, Haolong Yegorova, Yekaterina Hasegawa-Johnson, Mark Audio and Speech Processing Artificial Intelligence Computation and Language Machine Learning Children's speech recognition remains challenging due to substantial acoustic and linguistic variability, limited labeled data, and significant differences from adult speech. Speech foundation models can address these challenges through Speech In-Context Learning (SICL), allowing adaptation to new domains without fine-tuning. However, the effectiveness of SICL depends on how in-context examples are selected. We extend an existing retrieval-based method, Text-Embedding KNN for SICL (TICL), introducing an acoustic reranking step to create TICL+. This extension prioritizes examples that are both semantically and acoustically aligned with the test input. Experiments on four children's speech corpora show that TICL+ achieves up to a 53.3% relative word error rate reduction over zero-shot performance and 37.6% over baseline TICL, highlighting the value of combining semantic and acoustic information for robust, scalable ASR in children's speech. |
| title | TICL+: A Case Study On Speech In-Context Learning for Children's Speech Recognition |
| topic | Audio and Speech Processing Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2512.18263 |