# K-Function: Joint Pronunciation Transcription and Feedback for Evaluating Kids Language Function

arXiv: https://arxiv.org/abs/2507.03043 · HTML: https://mlsanity.com/paper/2507.03043/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 51 references that are in the map.
- First posted on arXiv 3 Jul 2025.

## Builds on (most cited first, 28 of 28)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [Self-Supervised Speech Representation Learning: A Review](https://mlsanity.com/paper/2205.10643/) (2022)
- [Benchmarking Children's ASR with Supervised and Self-supervised Speech Foundation Models](https://mlsanity.com/paper/2406.10507/) (2024)
- [Phone-to-audio alignment without text: A Semi-supervised Approach](https://mlsanity.com/paper/2110.03876/) (2021)
- [Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASR](https://mlsanity.com/paper/2305.00115/) (2023)
- [Unconstrained Dysfluency Modeling for Dysfluent Speech Transcription and Detection](https://mlsanity.com/paper/2312.12810/) (2023)
- [A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding](https://mlsanity.com/paper/2211.05869/) (2022)
- [Coding Speech through Vocal Tract Kinematics](https://mlsanity.com/paper/2406.12998/) (2024)
- [YOLO-Stutter: End-to-end Region-Wise Speech Dysfluency Detection](https://mlsanity.com/paper/2408.15297/) (2024)
- [Towards Hierarchical Spoken Language Dysfluency Modeling](https://mlsanity.com/paper/2401.10015/) (2024)
- [SSDM: Scalable Speech Dysfluency Modeling](https://mlsanity.com/paper/2408.16221/) (2024)
- [Deep Neural Convolutive Matrix Factorization for Articulatory Representation Decomposition](https://mlsanity.com/paper/2204.00465/) (2022)
- [Deep Speech Synthesis from MRI-Based Articulatory Representations](https://mlsanity.com/paper/2307.02471/) (2023)
- [Articulatory Representation Learning Via Joint Factor Analysis and Neural Matrix Factorization](https://mlsanity.com/paper/2210.16498/) (2022)
- [Stutter-Solver: End-to-end Multi-lingual Dysfluency Detection](https://mlsanity.com/paper/2409.09621/) (2024)
- [Analysis and Evaluation of Synthetic Data Generation in Speech Dysfluency Detection](https://mlsanity.com/paper/2505.22029/) (2025)
- [Analysis of Self-Supervised Speech Models on Children's Speech and Infant Vocalizations](https://mlsanity.com/paper/2402.06888/) (2024)
- [Analysis of Disfluency in Children's Speech](https://mlsanity.com/paper/2010.04293/) (2020)
- [Selective Attention Merging for low resource tasks: A case study of Child ASR](https://mlsanity.com/paper/2501.08468/) (2025)
- [Enhancing Child Vocalization Classification with Phonetically-Tuned Embeddings for Assisting Autism Diagnosis](https://mlsanity.com/paper/2309.07287/) (2023)
- [Dysfluent WFST: A Framework for Zero-Shot Speech Dysfluency Transcription and Detection](https://mlsanity.com/paper/2505.16351/) (2025)
- [Exploring Speech Foundation Models for Speaker Diarization in Child-Adult Dyadic Interactions](https://mlsanity.com/paper/2406.07890/) (2024)
- [Seamless Dysfluent Speech Text Alignment for Disordered Speech Analysis](https://mlsanity.com/paper/2506.12073/) (2025)
- [Causal Analysis of ASR Errors for Children: Quantifying the Impact of Physiological, Cognitive, and Extrinsic Factors](https://mlsanity.com/paper/2502.08587/) (2025)

## Built on by (most cited first, 1 of 1)

- [Revisiting Rule-Based Stuttering Detection: A Comprehensive Analysis of Interpretable Models for Clinical Applications](https://mlsanity.com/paper/2508.16681/) (2025)

## Abstract (arXiv)

Evaluating young children's language is challenging for automatic speech recognizers due to high-pitched voices, prolonged sounds, and limited data. We introduce K-Function, a framework that combines accurate sub-word transcription with objective, Large Language Model (LLM)-driven scoring. Its core, Kids-Weighted Finite State Transducer (K-WFST), merges an acoustic phoneme encoder with a phoneme-similarity model to capture child-specific speech errors while remaining fully interpretable. K-WFST achieves a 1.39 % phoneme error rate on MyST and 8.61 % on Multitudes-an absolute improvement of 10.47 % and 7.06 % over a greedy-search decoder. These high-quality transcripts are used by an LLM to grade verbal skills, developmental milestones, reading, and comprehension, with results that align closely with human evaluators. Our findings show that precise phoneme recognition is essential for creating an effective assessment framework, enabling scalable language screening for children.