Abstract
Speech segmentation is the process of dividing speech signals into parts, which is an important aspect of speaker identification and speech recognition systems. This process improves the efficiency of the system by accurately detecting the beginning and end of speech. The use of voice activity detectors (VADs) plays an important role in segmentation, as they help to determine the boundaries between speech and silence. However, the most common errors in segmentation are false positives and false negatives, which negatively affect the overall accuracy of the system. In this regard, it is necessary to reduce errors through various approaches and methods. Measures such as reducing background noise, using deep learning models, and data augmentation can significantly improve the quality of segmentation. Using spectral analysis methods and features allows you to clearly distinguish between speech and background noise. The purpose of this study is to optimize the segmentation process and analyze the probability of errors, improve the efficiency of speech recognition systems. As a result, this work provides a basis for new research and development in the field of speech recognition. The article considers the problem of speech segmentation for speaker identification. The paper describes possible segmentation criteria – qualitative and quantitative characteristics of sound speech, such as speech delays and intonation, as well as their acoustic relationship. This allows a specialist to identify specific segment units (syllables, words, etc.), record their structure, and identify the main features.