Whisper-Large for Child Speech Accuracy Classification
Model Description
This model includes the implementation of speech accuracy classification (half-children and half-adult) described in ChildVox: A Speech, Audio, and Large Audio-Language Model Benchmark in Understanding and Characterizing Sound across Childhood (Accepted to EMNLP 2026 Main)
Github repository: https://github.com/tiantiaf0627/childvox-release
The model is fine-tuned on the SpeechOcean762 dataset, a large-scale corpus of speech dataset with quality assessment (half children, half adult).
The included prosody categories are:
[
"Poor or Understandable", # <= 6 from original scores
"Good", # 7-8 from original scores
"Excellent" # >8 from original scores
]
How to use this model
Download repo
git clone git@github.com:tiantiaf0627/childvox-release
Install the package
conda create -n childvox python=3.10
cd childvox
pip install -e .
Load the model
# Load libraries
import torch
import torch.nn.functional as F
from src.model.childvox.whisper_audio import WhisperWrapper
# Find device
device = torch.device("cuda") if torch.cuda.is_available() else "cpu"
# Load model from Huggingface
# We provide model with different folds, and specify the fold from 1, 2, 3, 4, 5
model = WhisperWrapper.from_pretrained("tiantiaf/childvox-speechocean762-accuracy-whisper-large", fold_idx=1).to(device)
model.eval()
Prediction
# Label List
accuracy_list = [
"Poor or Understandable", # <= 6 from original scores
"Good", # 7-8 from original scores
"Excellent" # >8 from original scores
]
# Load data, here just zeros as the example
# The child speech segments used in training, which we cap the input at 10 seconds (even the model specify 15 seconds)
# You need to prepare your audio to a length of 10 seconds, 16kHz and mono channel
max_audio_length = 10 * 16000
data = torch.zeros([1, 160000]).float().to(device)[:, :max_audio_length]
logits, embeddings = model(data, return_feature=True)
# Probability and output
accuracy_prob = F.softmax(logits, dim=1)
print(accuracy_list[torch.argmax(accuracy_prob).detach().cpu().item()])
Responsible Use: Child speech data is highly sensitive. Users should respect the privacy and consent of the children and families whose recordings are processed, obtain approval from the appropriate ethics/IRB body, and adhere to the relevant laws and regulations in their jurisdictions when using ChildVox.
If you have any questions, please contact: Tiantian Feng (tiantiaf@usc.edu)
❌ Out-of-Scope Use
- Clinical or diagnostic applications (e.g., screening for developmental or language disorders)
- Individual-level developmental assessment without expert human review
- Surveillance
- Privacy-invasive applications
- No commercial use
If you like our work or use the models in your work, kindly cite the following. We appreciate your recognition!
@article{feng2026childvox,
title={ChildVox: A Speech, Audio, and Large Audio-Language Model Benchmark in Understanding and Characterizing Sound across Childhood},
author={Feng, Tiantian and Xu, Anfeng and Shi, Xuan and Kommineni, Aditya and Siam, Shakhrul Iman and Micheletti, Megan and Shi, Zhonghao and Tager-Flusberg, Helen and Zhang, Mi and Perry, Lynn K and others},
journal={arXiv preprint arXiv:2605.29257},
year={2026}
}
Please also cite the following dataset used to develop this model
@inproceedings{zhang21x_interspeech,
title = {{speechocean762: An Open-Source Non-Native English Speech Corpus for Pronunciation Assessment}},
author = {Junbo Zhang and Zhiwen Zhang and Yongqing Wang and Zhiyong Yan and Qiong Song and Yukai Huang and Ke Li and Daniel Povey and Yujun Wang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3710--3714},
doi = {10.21437/Interspeech.2021-1259},
issn = {2958-1796},
}