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Related Experiment Video

Updated: Sep 9, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

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Published on: August 9, 2024

Research on optimal deep learning modeling in HaiNan dialect recognition.

ZiXuan Qi^1,2, FuYun Li^3,4, HaiXia Long^5,6

¹College of Information Science and Technology, HaiNan Normal University, Haikou, 571158, China.

Scientific Reports

|August 28, 2025

Summary

This study introduces ConvMHANet, a novel deep learning model for HaiNan dialect speech recognition. The model achieves high accuracy in converting diverse dialects to Mandarin, overcoming data scarcity challenges.

Keywords:

Convolutional neural network HaiNan dialect Multi-classification Multi-head self-attention mechanism Speech recognition

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Last Updated: Sep 9, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

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Published on: August 9, 2024

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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Published on: April 14, 2023

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Area of Science:

Computational Linguistics
Artificial Intelligence
Speech Processing

Background:

HaiNan dialect speech recognition faces challenges due to significant phonological, intonational, and grammatical variations.
Current research is limited by insufficient corpus resources, particularly for multi-dialect recognition.
Traditional models struggle with data scarcity and diverse dialectal characteristics.

Purpose of the Study:

To explore deep learning models for HaiNan dialect-to-Mandarin conversion.
To identify the optimal deep learning model for HaiNan dialect recognition.
To propose an effective fusion model for multi-dialect recognition.

Main Methods:

Application of multiple deep learning models.
Extensive experimental comparative analysis.
Development of a fusion model combining Convolutional Neural Networks (CNNs) and Multi-Head Self-Attention Mechanism (MHAN).

Main Results:

The proposed fusion model, ConvMHANet, demonstrates excellent performance across different dialectal scenes.
ConvMHANet effectively addresses complex dialectal phonetic and contextual dependencies.
Achieved 97.58% accuracy and a character error rate of 0.0163 in a multi-classification mixing task.

Conclusions:

ConvMHANet shows strong generalization capabilities for HaiNan dialect speech recognition.
The fusion model offers a promising solution for dialect-to-Mandarin conversion tasks.
This research contributes to advancing speech recognition for low-resource dialects.