Publication

AIPNet: Generative Adversarial Pre-Training of Accent-Invariant Network for End-to-End Speech Recognition

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)


Abstract

As one of the major sources in speech variability, accents have posed a grand challenge to the robustness of speech recognition systems. In this paper, our goal is to build a unified end-to-end speech recognition system that generalizes well across accents. For this purpose, we propose a novel pre-training framework AIPNet based on generative adversarial nets (GAN) for accent-invariant representation learning: Accent Invariant Pre-training Networks. We pre-train AIPNet to disentangle accent-invariant and accent-specific characteristics from acoustic features through adversarial training on accented data for which transcriptions are not necessarily available. We further fine-tune AIPNet by connecting the accent-invariant module with an attention-based encoder-decoder model for multiaccent speech recognition. In the experiments, our approach is compared against four baselines including both accent-dependent and accent-independent models. Experimental results on 9 English accents show that the proposed approach outperforms all the baselines by 2.3 ∼ 4.5% relative reduction on average WER when transcriptions are available in all accents and by 1.6 ∼ 6.1% relative reduction when transcriptions are only available in US accent.

Related Publications

All Publications

Journal of Big Data - July 19, 2021

Cumulative deviation of a subpopulation from the full population

Mark Tygert

Interspeech - August 31, 2021

slimIPL: Language-Model-Free Iterative Pseudo-Labeling

Tatiana Likhomanenko, Qiantong Xu, Jacob Kahn, Gabriel Synnaeve, Ronan Collobert

NeurIPS - July 16, 2021

Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization

Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner

ICML - July 19, 2021

Making Paper Reviewing Robust to Bid Manipulation Attacks

Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy