Conformer-based Hybrid ASR System for Switchboard Dataset

• URL: http://arxiv.org/abs/2111.03442v1
• Date: Fri, 5 Nov 2021 12:03:18 GMT
• Title: Conformer-based Hybrid ASR System for Switchboard Dataset
• Authors: Mohammad Zeineldeen, Jingjing Xu, Christoph L\"uscher, Wilfried Michel, Alexander Gerstenberger, Ralf Schl\"uter, Hermann Ney
• Abstract summary: We present and evaluate a competitive conformer-based hybrid model training recipe. We study different training aspects and methods to improve word-error-rate as well as to increase training speed. We conduct experiments on Switchboard 300h dataset and our conformer-based hybrid model achieves competitive results.
• Score: 99.88988282353206
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recently proposed conformer architecture has been successfully used for end-to-end automatic speech recognition (ASR) architectures achieving state-of-the-art performance on different datasets. To our best knowledge, the impact of using conformer acoustic model for hybrid ASR is not investigated. In this paper, we present and evaluate a competitive conformer-based hybrid model training recipe. We study different training aspects and methods to improve word-error-rate as well as to increase training speed. We apply time downsampling methods for efficient training and use transposed convolutions to upsample the output sequence again. We conduct experiments on Switchboard 300h dataset and our conformer-based hybrid model achieves competitive results compared to other architectures. It generalizes very well on Hub5'01 test set and outperforms the BLSTM-based hybrid model significantly.

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