# TICL+: A Case Study On Speech In-Context Learning for Children's Speech Recognition

arXiv: https://arxiv.org/abs/2512.18263 · HTML: https://mlsanity.com/paper/2512.18263/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 20 Dec 2025.

## Builds on (most cited first, 17 of 17)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs](https://mlsanity.com/paper/2503.01743/) (2025)
- [Many-Shot In-Context Learning](https://mlsanity.com/paper/2404.11018/) (2024)
- [Whisper-AT: Noise-Robust Automatic Speech Recognizers are Also Strong General Audio Event Taggers](https://mlsanity.com/paper/2307.03183/) (2023)
- [Can Whisper perform speech-based in-context learning?](https://mlsanity.com/paper/2309.07081/) (2023)
- [My Science Tutor (MyST) -- A Large Corpus of Children's Conversational Speech](https://mlsanity.com/paper/2309.13347/) (2023)
- [Kid-Whisper: Towards Bridging the Performance Gap in Automatic Speech Recognition for Children VS. Adults](https://mlsanity.com/paper/2309.07927/) (2023)
- [Adaptation of Whisper models to child speech recognition](https://mlsanity.com/paper/2307.13008/) (2023)
- [Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASR](https://mlsanity.com/paper/2305.00115/) (2023)
- [DRAFT: A Novel Framework to Reduce Domain Shifting in Self-supervised Learning and Its Application to Children's ASR](https://mlsanity.com/paper/2206.07931/) (2022)
- [A Wav2vec2-Based Experimental Study on Self-Supervised Learning Methods to Improve Child Speech Recognition](https://mlsanity.com/paper/2204.05419/) (2022)
- [In-Context Learning Boosts Speech Recognition via Human-like Adaptation to Speakers and Language Varieties](https://mlsanity.com/paper/2505.14887/) (2025)
- [M2R-Whisper: Multi-stage and Multi-scale Retrieval Augmentation for Enhancing Whisper](https://mlsanity.com/paper/2409.11889/) (2024)
- [FASA: a Flexible and Automatic Speech Aligner for Extracting High-quality Aligned Children Speech Data](https://mlsanity.com/paper/2406.17926/) (2024)
- [Automatic Screening for Children with Speech Disorder using Automatic Speech Recognition: Opportunities and Challenges](https://mlsanity.com/paper/2410.11865/) (2024)
- [TICL: Text-Embedding KNN For Speech In-Context Learning Unlocks Speech Recognition Abilities of Large Multimodal Models](https://mlsanity.com/paper/2509.13395/) (2025)

## Built on by (most cited first, 4 of 4)

- [SALSA: Speech Aware LLM Adaptation via Learned Steering Activation Vectors](https://mlsanity.com/paper/2606.00460/) (2026)
- [MetaSICL: Globalizing Auditory LLMs for Underserved Speakers and Languages via Meta Speech In-Context Learning](https://mlsanity.com/paper/2601.18904/) (2026)
- [FSA-GRPO: Teaching Auditory LLMs to Use Few-Shot Demonstrations](https://mlsanity.com/paper/2606.02615/) (2026)
- [In-Context Adaptation of Encoder-Decoder Models in Speech Recognition](https://mlsanity.com/paper/2609.33865/) (2026)

## Abstract (arXiv)

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.