# Continuous Speech for Improved Learning Pathological Voice Disorders

arXiv: https://arxiv.org/abs/2202.10777 · HTML: https://mlsanity.com/paper/2202.10777/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 47 references that are in the map.
- First posted on arXiv 22 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Light Gated Recurrent Units for Speech Recognition](https://mlsanity.com/paper/1803.10225/) (2018)
- [Memory Visualization for Gated Recurrent Neural Networks in Speech Recognition](https://mlsanity.com/paper/1609.08789/) (2016)
- [Robustness against the channel effect in pathological voice detection](https://mlsanity.com/paper/1811.10376/) (2018)
- [Parameterization of Sequence of MFCCs for DNN-based voice disorder detection](https://mlsanity.com/paper/1812.05888/) (2018)
- [Pathological Voice Classification Using Mel-Cepstrum Vectors and Support Vector Machine](https://mlsanity.com/paper/1812.07729/) (2018)

## Built on by (most cited first, 4 of 4)

- [Sustained Vowels for Pre- vs Post-Treatment COPD Classification](https://mlsanity.com/paper/2406.06355/) (2024)
- [Towards Robust Assessment of Pathological Voices via Combined Low-Level Descriptors and Foundation Model Representations](https://mlsanity.com/paper/2505.21356/) (2025)
- [Enhancing dysarthria speech feature representation with empirical mode decomposition and Walsh-Hadamard transform](https://mlsanity.com/paper/2401.00225/) (2023)
- [AI-Driven Acoustic Voice Biomarker-Based Hierarchical Classification of Benign Laryngeal Voice Disorders from Sustained Vowels](https://mlsanity.com/paper/2512.24628/) (2025)

## Abstract (arXiv)

Goal: Numerous studies had successfully differentiated normal and abnormal voice samples. Nevertheless, further classification had rarely been attempted. This study proposes a novel approach, using continuous Mandarin speech instead of a single vowel, to classify four common voice disorders (i.e. functional dysphonia, neoplasm, phonotrauma, and vocal palsy). Methods: In the proposed framework, acoustic signals are transformed into mel-frequency cepstral coefficients, and a bi-directional long-short term memory network (BiLSTM) is adopted to model the sequential features. The experiments were conducted on a large-scale database, wherein 1,045 continuous speech were collected by the speech clinic of a hospital from 2012 to 2019. Results: Experimental results demonstrated that the proposed framework yields significant accuracy and unweighted average recall improvements of 78.12-89.27% and 50.92-80.68%, respectively, compared with systems that use a single vowel. Conclusions: The results are consistent with other machine learning algorithms, including gated recurrent units, random forest, deep neural networks, and LSTM. The sensitivities for each disorder were also analyzed, and the model capabilities were visualized via principal component analysis. An alternative experiment based on a balanced dataset again confirms the advantages of using continuous speech for learning voice disorders.