Advanced Artificial Intelligence and Machine Learning Models in Voice Profiling for Identification (Published)
Voice profiling for identification has undergone transformative advancement through the integration of artificial intelligence and machine learning methodologies, representing a paradigm shift from traditional spectrographic analysis and human expert interpretation. This technological evolution has addressed fundamental limitations of conventional approaches through deep neural architectures including convolutional neural networks and recurrent neural networks, which excel at extracting complex speech features. Speaker embedding techniques such as x-vectors, i-vectors, and d-vectors have revolutionized how variable-length utterances are transformed into fixed-dimensional representations, while attention mechanisms have dramatically enhanced model performance by focusing on the most discriminative portions of speech signals. These innovations enable practical applications across multiple domains, including frictionless customer authentication in financial services, sophisticated fraud detection systems capable of identifying synthetic speech attempts, and forensic voice analysis that provides quantifiable match confidence for legal proceedings.
Keywords: anti-spoofing, attention mechanisms, neural embeddings, speaker recognition, voice biometrics