Improving the Training Recipe for a Robust Conformer-based Hybrid Model
Abstract
Speaker adaptation is important to build robust automatic speech recognition (ASR) systems. In this work, we investigate various methods for speaker adaptive training (SAT) based on feature-space approaches for a conformer-based acoustic model (AM) on the Switchboard 300h dataset. We propose a method, called Weighted-Simple-Add, which adds weighted speaker information vectors to the input of the multi-head self-attention module of the conformer AM. Using this method for SAT, we achieve 3.5% and 4.5% relative improvement in terms of WER on the CallHome part of Hub5'00 and Hub5'01 respectively. Moreover, we build on top of our previous work where we proposed a novel and competitive training recipe for a conformer-based hybrid AM. We extend and improve this recipe where we achieve 11% relative improvement in terms of word-error-rate (WER) on Switchboard 300h Hub5'00 dataset. We also make this recipe efficient by reducing the total number of parameters by 34% relative.
Cite
@article{arxiv.2206.12955,
title = {Improving the Training Recipe for a Robust Conformer-based Hybrid Model},
author = {Mohammad Zeineldeen and Jingjing Xu and Christoph Lüscher and Ralf Schlüter and Hermann Ney},
journal= {arXiv preprint arXiv:2206.12955},
year = {2022}
}
Comments
Accepted at INTERSPEECH 2022