Audio Classification
speechbrain
English
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metadata
license: openrail
language:
  - en
metrics:
  - f1, UAR
library_name: speechbrain
pipeline_tag: audio-classification

Model Card for Model ID

We build a CTC-based ASR model using wav2vec 2.0 (W2V2) for children under 4-year-old. We use two-level fine-tuning to gradually reduce age mismatch between adult ASR to child ASR. We first fine-tune W2V2-LibriSpeech960h using My Science Tutor corpus (consists of conversational speech of students between the third and fifth grades with a virtual tutor) on character level. Then we fine-tune W2V2-MyST 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 or consonant/vowel sequences.
We show W2V2-Providence 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

  • Paper: Coming soon

Model Description

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

Paper/BibTex Citation

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

Coming soon

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: jialuli3@illinois.edu

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