A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks

Bibliographic Details
Title: A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks
Authors: Tang, Yun, Pino, Juan, Wang, Changhan, Ma, Xutai, Genzel, Dmitriy
Publication Year: 2020
Collection: Computer Science
Subject Terms: Computer Science - Computation and Language
More Details: Attention-based sequence-to-sequence modeling provides a powerful and elegant solution for applications that need to map one sequence to a different sequence. Its success heavily relies on the availability of large amounts of training data. This presents a challenge for speech applications where labelled speech data is very expensive to obtain, such as automatic speech recognition (ASR) and speech translation (ST). In this study, we propose a general multi-task learning framework to leverage text data for ASR and ST tasks. Two auxiliary tasks, a denoising autoencoder task and machine translation task, are proposed to be co-trained with ASR and ST tasks respectively. We demonstrate that representing text input as phoneme sequences can reduce the difference between speech and text inputs, and enhance the knowledge transfer from text corpora to the speech to text tasks. Our experiments show that the proposed method achieves a relative 10~15% word error rate reduction on the English Librispeech task compared with our baseline, and improves the speech translation quality on the MuST-C tasks by 3.6~9.2 BLEU.
Comment: Accepted for ICASSP2021
Document Type: Working Paper
Access URL: http://arxiv.org/abs/2010.11338
Accession Number: edsarx.2010.11338
Database: arXiv
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  Data: <searchLink fieldCode="AR" term="%22Tang%2C+Yun%22">Tang, Yun</searchLink><br /><searchLink fieldCode="AR" term="%22Pino%2C+Juan%22">Pino, Juan</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Changhan%22">Wang, Changhan</searchLink><br /><searchLink fieldCode="AR" term="%22Ma%2C+Xutai%22">Ma, Xutai</searchLink><br /><searchLink fieldCode="AR" term="%22Genzel%2C+Dmitriy%22">Genzel, Dmitriy</searchLink>
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  Data: Attention-based sequence-to-sequence modeling provides a powerful and elegant solution for applications that need to map one sequence to a different sequence. Its success heavily relies on the availability of large amounts of training data. This presents a challenge for speech applications where labelled speech data is very expensive to obtain, such as automatic speech recognition (ASR) and speech translation (ST). In this study, we propose a general multi-task learning framework to leverage text data for ASR and ST tasks. Two auxiliary tasks, a denoising autoencoder task and machine translation task, are proposed to be co-trained with ASR and ST tasks respectively. We demonstrate that representing text input as phoneme sequences can reduce the difference between speech and text inputs, and enhance the knowledge transfer from text corpora to the speech to text tasks. Our experiments show that the proposed method achieves a relative 10~15% word error rate reduction on the English Librispeech task compared with our baseline, and improves the speech translation quality on the MuST-C tasks by 3.6~9.2 BLEU.<br />Comment: Accepted for ICASSP2021
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