On Architectures and Training for Raw Waveform Feature Extraction in ASR

Vieting, Peter; Lüscher, Christoph; Michel, Wilfried; Schlüter, Ralf; Ney, Hermann

doi:10.1109/asru51503.2021.9688123

Cited by 3 publications

(1 citation statement)

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“…Recent developments in transfer learning have shown promising results in ASR, especially in recognizing speech from low-resource languages [10]- [12]. A key strategy involves finetuning an acoustic model.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%