Abstract
Patients who are subjected to intensive medical treatments in order to identify voice problems may suffer a considerable amount during the diagnostic process. As a result, automated speech recognition and methods for disorder diagnosis have garnered a lot of attention in recent years, and both of these approaches have been effective. Voice recordings were collected from the Saarbrucken Voice Database to help with the completion of this inquiry. To de-noise and remove any potential noise, the signals are first preprocessed using a hybrid Wiener filter discrete wavelet transform (HWFDWT). Melanie Frequency Cestrum Cat Swarm Optimization is used to extract features while accounting for coefficients. Finally, the features are arranged by running the data through the modified optimized back propagation network (MOBPN) for the categorization of disordered voices. In all aspects of precision, recall, F1-score, and timespan, the classification strategy exceeds the present Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) methodologies. Due to the cognitive speech system, people who lack the ability to speech can now convey their feelings and ideas to others in a form that can now communicate their thoughts and feelings to others in a way that is simple to grasp. It is a device that can record the electrical pulses the brain emits and then transform those records into synthetic speech. Provide a thorough explanation of the idea or solution you intend to develop. After recording the brain's electrical activity, it will be sent to a synthesizer for further processing. When it has completed decoding the signal, the synthesizer will transform it into a voice. After the voice has been decrypted, it is sent into an artificial voice box for further processing. Brain signals are employed to create an artificial voice. This voice is processed before being transmitted through the box.
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Chadha, R., Singla, S., Singh, N.T. (2023). An Artificial Voice Box that Makes Use of Unconventional Methods of Machine Learning. In: Murthy, B.K., Reddy, B.V.R., Hasteer, N., Van Belle, JP. (eds) Decision Intelligence. InCITe 2023. Lecture Notes in Electrical Engineering, vol 1079. Springer, Singapore. https://doi.org/10.1007/978-981-99-5997-6_3
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DOI: https://doi.org/10.1007/978-981-99-5997-6_3
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