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

arXiv: https://arxiv.org/abs/2205.11748 · HTML: https://mlsanity.com/paper/2205.11748/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 24 May 2022.

## Builds on (most cited first, 5 of 5)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [DenseNet: Implementing Efficient ConvNet Descriptor Pyramids](https://mlsanity.com/paper/1404.1869/) (2014)
- [Rethinking CNN Models for Audio Classification](https://mlsanity.com/paper/2007.11154/) (2020)
- [Data augmentation approaches for improving animal audio classification](https://mlsanity.com/paper/1912.07756/) (2019)
- [Disfluency Detection using Auto-Correlational Neural Networks](https://mlsanity.com/paper/1808.09092/) (2018)

## Abstract (arXiv)

This article describes a system for analyzing acoustic data to assist in the diagnosis and classification of children's speech sound disorders (SSDs) using a computer. The analysis concentrated on identifying and categorizing four distinct types of Chinese SSDs. The study collected and generated a speech corpus containing 2540 stopping, backing, final consonant deletion process (FCDP), and affrication samples from 90 children aged 3--6 years with normal or pathological articulatory features. Each recording was accompanied by a detailed diagnostic annotation by two speech-language pathologists (SLPs). Classification of the speech samples was accomplished using three well-established neural network models for image classification. The feature maps were created using three sets of Mel-frequency cepstral coefficients (MFCC) parameters extracted from speech sounds and aggregated into a three-dimensional data structure as model input. We employed six techniques for data augmentation to augment the available dataset while avoiding overfitting. The experiments examine the usability of four different categories of Chinese phrases and characters. Experiments with different data subsets demonstrate the system's ability to accurately detect the analyzed pronunciation disorders. The best multi-class classification using a single Chinese phrase achieves an accuracy of 74.4~percent.