Abstract
In addition to visual and auditory evaluation, forensic audio experts use automatic speaker verification systems to analyze forensic voice evidence. Gaussian Mixture Model is widely used in such systems. Recently, the usage of i-vector, derived from factor analysis, has gained popularity. In this work, performance comparison of Gaussian Mixture Model and i-vector methods on a speaker verification system is conducted on short recordings of speech. The experimental results revealed that the system that uses i-vector produces a much lower equal error rate.
Keywords
Speaker verification
Computer science
Mixture model
Speech recognition
Speaker recognition
Word error rate
Pattern recognition (psychology)
Biometrics
Speaker diarisation
Gaussian
Artificial intelligence
Support vector machine
Subject Areas
Speech Recognition and Synthesis
·Artificial Intelligence ·Physical Sciences
Speech and Audio Processing
·Signal Processing ·Physical Sciences
Music and Audio Processing
·Signal Processing ·Physical Sciences
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