What techniques improve automatic classification of Chinese dialects

The automatic classification of Chinese dialects is a challenging task due to the high linguistic complexity and subtle differences among dialects. Several techniques have been developed and studied to improve the accuracy and effectiveness of such classification systems.

Key Techniques to Improve Classification of Chinese Dialects

1. Lexical and Phonological Feature Extraction:

   • Using lexical features (words and characters) and phonological information to capture distinctive dialect characteristics.
   • Employing interpretable dialect classifiers that extract distinguishing lexical features for better separation of dialect varieties. ¹

2. Deep Learning-Based Methods:

   • Combining architectures such as Deep Convolutional Neural Networks (DCNN), Long Short-Term Memory (LSTM), and Deep Neural Networks (DNN) to model complex patterns in speech and text data effectively.
   • Data augmentation techniques to increase the dataset size and avoid overfitting when training deep learning models. ^{2, 3}

3. Text Segmentation and Language Models:

   • Utilizing Chinese tokenization and language models to better represent and differentiate dialect-specific language use.
   • Incorporating character-level and word-level features into classifiers to improve performance. ⁴

4. Glyph-Aware and Dictionary-Enhanced Models:

   • Chinese characters’ internal glyph structures and dictionary knowledge are used to enhance semantic representation in classification tasks.
   • Light-weight ensemble learning methods for glyph-aware Chinese text classification balance performance with computational costs. ^{5, 6}

5. Phonological Representation and Knowledge Graphs:

   • Constructing phonological knowledge graphs to obtain multi-dialectal representations of Chinese syllables.
   • Using unsupervised clustering and classifiers on these representations to capture phonemic contrasts between dialects. ⁷

6. Naive Bayes and Traditional Machine Learning:

   • Classical machine learning algorithms such as Naive Bayes combined with text preprocessing, feature selection, and lexical analysis show good performance for dialect classification. ⁸

7. Contextual Semantic and Structural Modeling:

   • Leveraging models like BERT (Bidirectional Encoder Representations from Transformers) combined with Graph Convolutional Networks (GCN) to encode contextual semantic and structural relationships in text for improved dialect classification accuracy. ⁹

Summary

Improvements in automatic classification of Chinese dialects largely rely on combining multiple feature types (lexical, phonological, and glyph-based) with advanced deep learning and machine learning techniques. Data augmentation, interpretable classifiers, and the use of both character-level and syllable-level representations also contribute significantly to better distinguishing between dialects.

These approaches together enhance the ability to capture subtle linguistic differences across Chinese dialects and increase classification accuracy and robustness. ^{1, 2, 4, 5, 7, 9}

References

• Extracting Lexical Features from Dialects via Interpretable Dialect Classifiers

• DLD: An Optimized Chinese Speech Recognition Model Based on Deep Learning

• Deep-Learning-Based Automated Classification of Chinese Speech Sound Disorders

• A Study About Chinese Dialect Identification Based on Tokenization and Language Model

• Assemble the shallow or integrate a deep? Toward a lightweight solution for glyph-aware Chinese text classification

• Shuo Wen Jie Zi: Rethinking Dictionaries and Glyphs for Chinese Language Pre-training

• Multi-Dialectal Representation Learning of Sinitic Phonology

• A Chinese text classification system based on Naive Bayes algorithm

• Chinese text classification by combining Chinese-BERTology-wwm and GCN

• Developing Effective Techniques for the Recognition of Shanghai Dialect Text

• Dialect Classification via Text-Independent Training and Testing for Arabic, Spanish, and Chinese

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• Deep Learning-based automated classification of Chinese Speech Sound Disorders

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• Entropy-based discrimination between translated Chinese and original Chinese using data mining techniques

• Sentence-level dialects identification in the greater China region

• On the Effectiveness of Pinyin-Character Dual-Decoding for End-to-End Mandarin Chinese ASR

• Bailing-TTS: Chinese Dialectal Speech Synthesis Towards Human-like Spontaneous Representation

• Chinese Language Feature Analysis Based on Multilayer Self-Organizing Neural Network and Data Mining Techniques

• A Machine Learning Classification Algorithm for Vocabulary Grading in Chinese Language Teaching