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International Journal of Speech Technology
Erschienen in: International Journal of Speech Technology 2/2023

26.06.2023

A transformer-based network for speech recognition

verfasst von: Lina Tang

Erschienen in: International Journal of Speech Technology | Ausgabe 2/2023

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Abstract

In the field of automatic speech recognition (ASR), the noisy audio data and the ambiguity in recognizing homophone lead to the degradation of model performance. In order to address the mentioned problems, a network called DMRS-transformer, a Transformer-based network, is proposed in this study. The proposed DMRS-Transformer mainly consists of two components except for the traditional Transformer network, which are denoising module and Mandarin recognition supplementary module respectively. The denoising module is used for pruning the trivial features caused by the noisy input audio data. The Mandarin recognition supplementary module, short for MRS module, tends to tackle the problem of recognizing Mandarin speech signals which have several homophones. Empirical evaluations have been conducted on two widely used datasets, which are Aishell-1 and HKUST respectively. The experimental results can validate the effectiveness of the proposed DMRS-Transformer network. Compared with the Transformer baseline, the proposed DMRS-Transformer has 0.8% CER improvement and 1.5% CER improvement in these two datasets respectively.
Vorheriger Artikel Stuttering detection using speaker representations and self-supervised contextual embeddings
Nächster Artikel An efficient speech emotion recognition based on a dual-stream CNN-transformer fusion network

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Metadaten
Titel
A transformer-based network for speech recognition
verfasst von
Lina Tang
Publikationsdatum
26.06.2023
Verlag
Springer US
Erschienen in
International Journal of Speech Technology / Ausgabe 2/2023
Print ISSN: 1381-2416
Elektronische ISSN: 1572-8110
DOI
https://doi.org/10.1007/s10772-023-10034-z

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