تقرير
Improving Self-supervised Pre-training using Accent-Specific Codebooks
| العنوان: | Improving Self-supervised Pre-training using Accent-Specific Codebooks |
|---|---|
| المؤلفون: | Prabhu, Darshan, Gupta, Abhishek, Nitsure, Omkar, Jyothi, Preethi, Ganapathy, Sriram |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing |
| الوصف: | Speech accents present a serious challenge to the performance of state-of-the-art end-to-end Automatic Speech Recognition (ASR) systems. Even with self-supervised learning and pre-training of ASR models, accent invariance is seldom achieved. In this work, we propose an accent-aware adaptation technique for self-supervised learning that introduces a trainable set of accent-specific codebooks to the self-supervised architecture. These learnable codebooks enable the model to capture accent specific information during pre-training, that is further refined during ASR finetuning. On the Mozilla Common Voice dataset, our proposed approach outperforms all other accent-adaptation approaches on both seen and unseen English accents, with up to 9% relative reduction in word error rate (WER). Comment: Accepted to INTERSPEECH 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.03734 |
| رقم الأكسشن: | edsarx.2407.03734 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |