Affiliation:
1. KPR Institute of Engineering and Technology, India
Abstract
The detection of voice pathology is a critical field in the domain of speech and healthcare, with early and accurate diagnosis being pivotal for effective treatment. Electroglottography (EGG) has been emerged as a promising tool for understanding the functioning of the vocal folds, offering valuable insights into voice disorders. This chapter highlights the current state of research in voice pathology detection using deep networks applied to EGG signals and examines various studies and methodologies in this area, emphasizing data collection and pre-processing techniques, the design of CNN architectures, training strategies, and performance evaluation metrics. Additionally, the chapter discusses the potential for further advancements, challenges, and opportunities in the field, emphasizing the importance of standardized datasets and the integration of CNN-based voice pathology detection models into clinical practice.
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