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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
LSTM Architecture Diagram

Figure 2.

DMBO Framework Architecture
DMBO Framework Architecture

Figure 3.

Overview of the E2E Speech Recognition Framework with DMBO
Overview of the E2E Speech Recognition Framework with DMBO

Figure 4.

Gender-based distribution of datasets samples
Gender-based distribution of datasets samples

Figure 5.

Accent-based distribution of datasets samples
Accent-based distribution of datasets samples

Figure 6.

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

Figure 7.

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