|
--- |
|
license: openrail |
|
language: |
|
- en |
|
metrics: |
|
- f1 |
|
- recall |
|
- accuracy |
|
library_name: speechbrain |
|
pipeline_tag: audio-classification |
|
--- |
|
# Model Card for Model ID |
|
|
|
<!-- Provide a quick summary of what the model is/does. --> |
|
|
|
We build a CTC-based phoneme recognition model using wav2vec 2.0 (W2V2) for children under 4-year-old. We use three-level fine-tuning to gradually reduce age mismatch between adult phonetics to child phonetics. |
|
|
|
- **W2V2-Libri100h**: We first fine-tune W2V2-Base using 100 hours of LibriSpeech pretrained on unlabeled 960 hours LibriSpeech adult speech corpus with IPA phone sequences. |
|
- **W2V2-MyST**: We then fine-tune W2V2-Libri100h using [My Science Tutor](https://boulderlearning.com/products/myst/) corpus (consists of conversational speech of students between the third and fifth grades with a virtual tutor). |
|
- **W2V2-Libri100h-Pro (two-level fine-tuning)**: We fine-tune W2V2-Libri100h using [Providence](https://phonbank.talkbank.org/access/Eng-NA/Providence.html) corpus (consists of longititude audio of 6 English-speaking children aged from 1-4 years interacting with their mothers at home) on phoneme sequences. |
|
- **W2V2-MyST-Pro (three-level fine-tuning)**: Similar as W2V2-Libri100h-Pro, we fine-tune W2V2-MyST using Providence on phoneme sequences. |
|
|
|
We show W2V2-MyST-Pro is helpful for improving children's vocalization classification task on two corpus, including [Rapid-ABC](https://openaccess.thecvf.com/content_cvpr_2013/html/Rehg_Decoding_Childrens_Social_2013_CVPR_paper.html) and [BabbleCor](https://osf.io/rz4tx/). |
|
|
|
## Model Sources |
|
For more information regarding this model, please checkout our paper: (TO-DO) |
|
- **Paper:** https://arxiv.org/pdf/2309.07287.pdf |
|
|
|
## Model Description |
|
|
|
<!-- Provide a longer summary of what this model is. --> |
|
Folder contains the best checkpoint of the following setting |
|
- **W2V2-MyST by fine-tuning on Librispeech 960h**: save_960h/wav2vec2.ckpt |
|
- **W2V2-Pro trained on phone sequence**: save_MyST_Providence_ep45_filtered/wav2vec2.ckpt |
|
- **W2V2-Pro trained on consonant/vowel sequence**: save_MyST_Providence_ep45_filtered_cv_only/wav2vec2.ckpt |
|
|
|
## Uses |
|
**We develop our complete fine-tuning recipe using SpeechBrain toolkit available at** |
|
|
|
- **https://github.com/jialuli3/speechbrain/tree/infant-voc-classification/recipes/RABC** (used for Rapid-ABC corpus) |
|
- **https://github.com/jialuli3/speechbrain/tree/infant-voc-classification/recipes/Babblecor** (used for BabbleCor corpus) |
|
|
|
# Paper/BibTex Citation |
|
|
|
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
|
If you found this model helpful to you, please cite us as |
|
<pre><code> |
|
@article{li2023enhancing, |
|
title={Enhancing Child Vocalization Classification in Multi-Channel Child-Adult Conversations Through Wav2vec2 Children ASR Features}, |
|
author={Li, Jialu and Hasegawa-Johnson, Mark and Karahalios, Karrie}, |
|
journal={arXiv preprint arXiv:2309.07287}, |
|
year={2023} |
|
} |
|
</code></pre> |
|
|
|
# Model Card Contact |
|
Jialu Li (she, her, hers) |
|
|
|
Ph.D candidate @ Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign |
|
|
|
E-mail: [email protected] |
|
|
|
Homepage: https://sites.google.com/view/jialuli/ |
