A Wav2vec2-Based Experimental Study on Self-Supervised Learning Methods to Improve Child Speech Recognition

Jain, Rishabh; Yiwere, Mariam; Bigioi, Dan; Corcoran, Peter

doi:10.48550/arxiv.2204.05419

Cited by 1 publication

(3 citation statements)

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“…All the audio data was converted to a 16-bit mono channel with a 16Khz sampling rate and saved as '.wav' audio files, while the transcriptions were saved as '.txt' files. Child data-specific cleaning methodology was kept consistent with [34]. Given the low resource nature of non-native child speech datasets, we opted to split the available data into 80% for testing and 20% for training.…”

Section: A Dataset Cleaning and Descriptionmentioning

confidence: 99%

“…2) My Science Tutor (MyST) Corpus [29] is an American English child speech dataset containing over 393 hours of audio data out of which 197 hours are fully transcribed. We use the cleaned version of this dataset (as described in [34]), with 65 hours of speech divided into two subsets: 55 hours for training, called 'MyST_train' and 10 hours for testing, called 'MyST_test'.…”

Section: A Dataset Cleaning and Descriptionmentioning

confidence: 99%

“…3) PFSTAR Corpus [30] contains a collection of words spoken by native British English children and non-native English child speech from Swedish, German, and Italian natives. The cleaned PFSTAR British dataset (as described in [34]) contains a total of 12 hours of usable audio. This data was divided into 10 hours for training, called 'PF_br_train', and 2 hours for testing, called 'PF_br_test'.…”

Section: A Dataset Cleaning and Descriptionmentioning

confidence: 99%

See 2 more Smart Citations

Exploring Native and Non-Native English Child Speech Recognition With Whisper

Jain,

Barcovschi,

Yiwere

et al. 2024

IEEE Access

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Modern end-to-end Automatic Speech Recognition (ASR) systems struggle to recognise children's speech. This challenge is due to the high acoustic variability in children's voices and the scarcity of child speech training data, particularly for accented or low-resource languages. This study focuses on improving the performance of ASR on native and non-native English child speech using publicly available datasets. We evaluate how the large-scale whisper models (trained with a large amount of adult speech data) perform with child speech. In addition, we perform finetuning experiments using different child speech datasets to investigate the performance of whisper ASR on non-native English-speaking children's speech. Our findings indicate relative Word Error Rate (WER) improvements ranging from 29% to 89% over previous benchmarks on the same datasets. Notably, these gains were achieved by finetuning with only a 10% sample of unseen nonnative datasets. These results demonstrate the potential of whisper for improving ASR in a low-resource scenario for nonnative child speech.

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Section: A Dataset Cleaning and Descriptionmentioning

confidence: 99%

Section: A Dataset Cleaning and Descriptionmentioning

confidence: 99%