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Stammering (stuttering) is a common speech disorder that may continue until adulthood, if not treated in its early stages. Stammering can significantly impact the quality of a person¿s life. Stammerers may affect in refraining from speaks and may waste opportunities to make friends, present own ideas and opinions in public, and negatively influence in job interviews. This study uses a dataset that involves speech signals with stammering, it can be called the Fluency Bank (FB). In this work, an efficient algorithm to perform stammering corrections (anti-stammering) is proposed. This algorithm contains an advanced techniques. These advanced techniques are the Enhanced One-dimensional Local Binary Patterns (EOLBP) and Adapted Multi-Layer Perceptron for Regression (AMLPR).The EOLBP is a useful feature extraction and the AMLPR is a generative classifier. Besides, choral or feedback speech is supplied by using these advanced techniques through feeding the algorithm¿s output (anti-stammered speech) to the stammerer¿s ear to provide the choral effect. This choral effect can produce a fluent speech via the stammerer¿s mouth that can contribute to curing people who stutter.
| Autorius: | Ali Hussein, Raid Al-Nima, Tingting Han, |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2024 |
| Knygos puslapių skaičius: | 88 |
| ISBN-10: | 6207484754 |
| ISBN-13: | 9786207484751 |
| Formatas: | 220 x 150 x 6 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Investigating Various Techniques for Overcoming Stammers / Stutters“
