Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
This project introduces a speaker recognition system built upon advanced techniques in audio signal analysis and machine learning. It focuses on identifying individual speakers by analyzing their voice patterns from recorded .wav files. The system extracts a range of meaningful features from the audio, including Mel-Frequency Cepstral Coefficients (MFCCs), spectral centroid, pitch, and chroma characteristics. These features are then fed into a Gradient Boosting Classifier, which is trained using a labeled dataset of voice samples. The model demonstrates strong performance in accurately distinguishing between different speakers. Designed with adaptability in mind, the system holds promise for real-world use cases such as integration into voice-based authentication platforms and smart, voice-responsive applications.
"Voice Classification System Using Gradient Boosting Method", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.a821-a826, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505097.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator