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Speech Processing Using Dynamic Micro-Block Optimization Based on Deep Learning Cover

Speech Processing Using Dynamic Micro-Block Optimization Based on Deep Learning

By: Jiajun Hao and  Chaoyang Geng  
Open Access
|Dec 2025

Figures & Tables

Figure 1.

LSTM Architecture Diagram

Figure 2.

DMBO Framework Architecture

Figure 3.

Overview of the E2E Speech Recognition Framework with DMBO

Figure 4.

Gender-based distribution of datasets samples

Figure 5.

Accent-based distribution of datasets samples

Figure 6.

Train loss (left), LER (right) for gender-based strategies

Figure 7.

Train loss (left), LER (right) for accent-based strategies

Train and test loss and ler of strategies based on accent

ModelTest LER
Standard14.62%
Homogeneous gender6.71%
Heterogeneous gender13.13%
Homogeneous accent13.58%
Heterogeneous accent5.51%
Attention-LSTM[21]9.77%
CNN-LSTM[22]14.05%
BLSTM[23]12.9%
BiLSTM-E[24]8.07%

Loss, LER for accent-based strategies during both train and test

Test LossTest LERTrain LossTrain LER
standard25.6914.62%12.7211.14%
Homogeneous accent22.2613.58%11.6410.04%
Heterogeneous accent12.085.51%5.704.42%

Loss, LER for gender-based strategies during both train and test

Test LossTest LERTrain LossTrain LER
Standard25.6914.62%12.7211.14%
Homogeneous gender13.536.71%6.155.65%
Heterogeneous gender24.1513.12%11.089.21%
Language: English
Page range: 46 - 58
Published on: Dec 31, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Jiajun Hao, Chaoyang Geng, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.