Accented Speech Recognition With Accent-specific Codebooks

Darshan Deepak Prabhu, Preethi Jyothi, Sriram Ganapathy, Vinit Unni

Submission Type: Regular Long Paper

Submission Track: Speech and Multimodality

Keywords: accented speech recognition, cross-attention, codebooks, conformer, domain adaptation

TL;DR: We propose a new technique based on quantized accent codebooks to improve accent adaptation for ASR.

Abstract: Speech accents pose a significant challenge to state-of-the-art automatic speech recognition (ASR) systems. Degradation in performance across underrepresented accents is a severe deterrent to the inclusive adoption of ASR. In this work, we propose a novel accent adaptation approach for end-to-end ASR systems using cross-attention with a trainable set of codebooks. These learnable codebooks capture accent-specific information and are integrated within the ASR encoder layers. The model is trained on accented English speech, while the test data also contained accents which were not seen during training. On the Mozilla Common Voice multi-accented dataset, we show that our proposed approach yields significant performance gains not only on the seen English accents (up to 37% relative improvement in word error rate) but also on the unseen accents (up to 5% relative improvement in WER). Further, we illustrate benefits for a zero-shot transfer setup on the L2Artic dataset. We also compare the performance with other approaches based on accent adversarial training.

Submission Number: 2428