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- W4386875331 abstract "Recent end-to-end automatic speech recognition (ASR) models have become increasingly larger, making them particularly challenging to be deployed on resource-constrained devices. Model quantisation is an effective solution that sometimes causes the word error rate (WER) to increase. In this paper, a novel strategy of personalisation for a quantised model (PQM) is proposed, which combines speaker adaptive training (SAT) with model quantisation to improve the performance of heavily compressed models. Specifically, PQM uses a 4-bit NormalFloat Quantisation (NF4) approach for model quantisation and low-rank adaptation (LoRA) for SAT. Experiments have been performed on the LibriSpeech and the TED-LIUM 3 corpora. Remarkably, with a 7x reduction in model size and 1% additional speaker-specific parameters, 15.1% and 23.3% relative WER reductions were achieved on quantised Whisper and Conformer-based attention-based encoder-decoder ASR models respectively, comparing to the original full precision models." @default.
- W4386875331 created "2023-09-20" @default.
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- W4386875331 date "2023-09-16" @default.
- W4386875331 modified "2023-09-27" @default.
- W4386875331 title "Enhancing Quantised End-to-End ASR Models via Personalisation" @default.
- W4386875331 doi "https://doi.org/10.48550/arxiv.2309.09136" @default.
- W4386875331 hasPublicationYear "2023" @default.
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