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Computational Intelligence-Based Stuttering Detection: A Systematic Review
DOI:10.3390/diagnostics13233537.png)
摘要
En 中文
Stuttering is a widespread speech disorder affecting people globally, and it impacts effective communication and quality of life. Recent advancements in artificial intelligence (AI) and computational intelligence have introduced new possibilities for augmenting stuttering detection and treatment procedures. In this systematic review, the latest AI advancements and computational intelligence techniques in the context of stuttering are explored. By examining the existing literature, we investigated the application of AI in accurately determining and classifying stuttering manifestations. Furthermore, we explored how computational intelligence can contribute to developing innovative assessment tools and intervention strategies for persons who stutter (PWS). We reviewed and analyzed 14 refereed journal articles that were indexed on the Web of Science from 2019 onward. The potential of AI and computational intelligence in revolutionizing stuttering assessment and treatment, which can enable personalized and effective approaches, is also highlighted in this review. By elucidating these advancements, we aim to encourage further research and development in this crucial area, enhancing in due course the lives of PWS.
Keyword:
stuttering detection
systematic review
rehabilitation
machine learning
期刊
IF:
3.3
论文数:
2.0W
被引数:
3.6W
机构
引用论文
FluentNet: End-to-End Detection of Stuttered Speech Disfluencies With Deep LearningFluentNet: 使用深度学习端到端检测口吃语音不流畅
Improved speech emotion recognition with Mel frequency magnitude coefficient改进的Mel频率幅度系数语音情感识别
APPLIED ACOUSTICS
IF3.6

