metadata

license: openrail
language:
  - en
metrics:
  - f1
  - recall
  - accuracy
library_name: speechbrain
pipeline_tag: audio-classification

Model Card for Model ID

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 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 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 and BabbleCor.

Model Sources

For more information regarding this model, please checkout our paper:

Model Description

Folder contains the best checkpoint of the following setting

W2V2-Libri100h: save_100h/wav2vec2.ckpt
W2V2-MyST: save_100h_MyST/wav2vec2.ckpt
W2V2-Libri100h-Pro: save_100h_Providence/wav2vec2.ckpt
W2V2-MyST-Pro: save_100h_MyST_Providence/wav2vec2.ckpt

Uses

We develop our complete fine-tuning recipe using SpeechBrain toolkit available at TO DO

Paper/BibTex Citation

If you found this model helpful to you, please cite us as


@article{li2023enhancing,
  title={Enhancing Child Vocalization Classification with Phonetically-Tuned Embeddings for Assisting Autism Diagnosis},
  author={Li, Jialu and Hasegawa-Johnson, Mark and Karahalios, Karrie},
  booktitle={Interspeech},
  year={2024}
}

and/or


@inproceedings{li2024analysis,
  title={Analysis of Self-Supervised Speech Models on Children's Speech and Infant Vocalizations},
  author={Li, Jialu and Hasegawa-Johnson, Mark and McElwain, Nancy L},
  booktitle={IEEE Workshop on Self-Supervision in Audio, Speech and Beyond (SASB)},
  year={2024}
}

Model Card Contact

Jialu Li, Ph.D. (she, her, hers)

E-mail: jialuli3@illinois.edu

Homepage: https://sites.google.com/view/jialuli/