File size: 2,576 Bytes
a548b2a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
---
license: openrail
language:
- en
metrics:
- f1, UAR
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 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](https://boulderlearning.com/products/myst/) 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](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 or consonant/vowel sequences.  
We show W2V2-Providence 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
- **Paper:** Coming soon
  
## 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

Coming soon
<!-- <pre><code>
  </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: jialuli3@illinois.edu

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