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--- |
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license: openrail |
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language: |
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- en |
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metrics: |
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- f1 |
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- recall |
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- accuracy |
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library_name: speechbrain |
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pipeline_tag: audio-classification |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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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. |
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- **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. |
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- **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). |
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- **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. |
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- **W2V2-MyST-Pro (three-level fine-tuning)**: Similar as W2V2-Libri100h-Pro, we fine-tune W2V2-MyST using Providence on phoneme sequences. |
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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/). |
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## Model Sources |
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For more information regarding this model, please checkout our paper: (TO-DO) |
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- **Paper:** https://arxiv.org/pdf/2309.07287.pdf |
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## Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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Folder contains the best checkpoint of the following setting |
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- **W2V2-MyST by fine-tuning on Librispeech 960h**: save_960h/wav2vec2.ckpt |
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- **W2V2-Pro trained on phone sequence**: save_MyST_Providence_ep45_filtered/wav2vec2.ckpt |
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- **W2V2-Pro trained on consonant/vowel sequence**: save_MyST_Providence_ep45_filtered_cv_only/wav2vec2.ckpt |
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## Uses |
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**We develop our complete fine-tuning recipe using SpeechBrain toolkit available at** |
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- **https://github.com/jialuli3/speechbrain/tree/infant-voc-classification/recipes/RABC** (used for Rapid-ABC corpus) |
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- **https://github.com/jialuli3/speechbrain/tree/infant-voc-classification/recipes/Babblecor** (used for BabbleCor corpus) |
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# Paper/BibTex Citation |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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If you found this model helpful to you, please cite us as |
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<pre><code> |
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@article{li2023enhancing, |
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title={Enhancing Child Vocalization Classification in Multi-Channel Child-Adult Conversations Through Wav2vec2 Children ASR Features}, |
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author={Li, Jialu and Hasegawa-Johnson, Mark and Karahalios, Karrie}, |
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journal={arXiv preprint arXiv:2309.07287}, |
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year={2023} |
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} |
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</code></pre> |
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# Model Card Contact |
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Jialu Li (she, her, hers) |
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Ph.D candidate @ Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign |
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E-mail: jialuli3@illinois.edu |
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Homepage: https://sites.google.com/view/jialuli/ |
