RESEARCH

NLP

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

April 24, 2020

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.

Download the Paper

AUTHORS

Written by

Yi-Chen Chen

Zhaojun Yang

Ching-Feng Yeh

Mahaveer Jain

Michael L. Seltzer

Publisher

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

Recent Publications

January 01, 2021

Asynchronous Gradient-Push | Facebook AI Research

We consider a multi-agent framework for distributed optimization where each agent has access to a local smooth strongly convex function, and the collective goal is to achieve consensus on the parameters that minimize the sum of the agents’…

Mahmoud Assran, Michael Rabbat

January 01, 2021

August 22, 2020

GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce

In this paper we propose image classification modeling technique targeted for marketplace. We use public posts from marketplace and search log interactions for training image classifier and achieve significant improvements in e-commerce in comparison to previous version of our image classifier.

Sean Bell, Yiqun Liu, Sami Alsheikh, Yina Tang, Ed Pizzi, M. Henning, Karun Singh, Omkar Parkhi, Fedor Borisyuk

August 22, 2020

June 16, 2020

COMPUTER VISION

PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization

Due to memory limitations in current hardware, previous approaches tend to take low resolution images as input to cover large spatial context, and produce less precise (or low resolution) 3D estimates as a result. We address this limitation by formulating a multi-level architecture that is end-to-end trainable

Shunsuke Saito, Tomas Simon, Jason Saragih, Hanbyul Joo

June 16, 2020

June 14, 2020

Iterative Answer Prediction with Pointer-Augmented Multimodal Transformers for TextVQA | Facebook AI Research

Many visual scenes contain text that carries crucial information, and it is thus essential to understand text in images for downstream reasoning tasks. For example, a deep water label on a warning sign warns people about the danger in the…

Ronghang Hu, Amanpreet Singh, Trevor Darrell, Marcus Rohrbach

June 14, 2020

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.