A customized residual neural network and bi-directional gated recurrent unit-based automatic speech recognition model

• Type: Article in International Scientific Journal
• Date: 2022-04

Authors

Selim Reza (Author) Other; Marta Campos Ferreira (Author) FEUP; J.J.M. Machado (Author) FEUP; João Manuel R. S. Tavares (Author) FEUP

Journal

The Journal is awaiting validation by the Administrative Services.

Title: EXPERT SYSTEMS WITH APPLICATIONS

Vol. 215

Pages: 1-10

ISSN: 0957-4174

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ISI Web of Knowledge - 0 Citations

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Scopus - 1 Citation

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Scientific classification

CORDIS: Technological sciences

FOS: Engineering and technology

Associated Projects

Safe Cities - Safe Cities - Inovação para Construir Cidades Seguras

Other information

Authenticus ID: P-00X-FZ7

DOI: 10.1016/j.eswa.2022.119293

Abstract (EN): Speech recognition aims to convert human speech into text and has applications in security, healthcare, commerce, automobiles, and technology, just to name a few. Inserting residual neural networks before recurrent neural network cells improves accuracy and cuts training time by a good margin. Furthermore, layer normalization instead of batch normalization is more effective in model training and performance enhancement. Also, the size of the datasets presents tremendous influences in achieving the best performance. Leveraging these tricks, this article proposes an automatic speech recognition model with a stacked five layers of customized Residual Convolution Neural Network and seven layers of Bi-Directional Gated Recurrent Units, including a logarithmic so f tmax for the model output. Each of them incorporates a learnable per-element affine parameter-based layer normalization technique. The training and testing of the new model were conducted on the LibriSpeech corpus and LJ Speech dataset. The experimental results demonstrate a character error rate (CER) of 4.7 and 3.61% on the two datasets, respectively, with only 33 million parameters without the requirement of any external language model.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10