Improving Speaker Verification in Noisy Environments using Adaptive Filtering and Hybrid Classification Technique
This study describes two approaches of improving speaker
verification in noisy environments. The first approach is implementation
of a speaker verification classification technique base on hybrid Vector
Quantization (VQ) and Hidden Markov Models (HMMs) in clean and noisy environments.
The second approach is implementation of Adaptive Noise Cancelation (ANC)
as pre-processing for noise removal. The motivation to implement hybrid
classification technique is to improve the HMMs performance. It is shown
that, by using the hybrid technique, an Equal Error Rate (EER) of 11.72%
is achieved compared to HMM alone, which achieved 16.66% in clean environments.
However, both techniques show degradation in noisy environments. In order
to address these problems, an Adaptive Noise Cancellation (ANC) technique
using adaptive filtering is implemented in the pre-processing stage due
to its ability to separate overlapping speech frequency bands. Investigations
using Least-Mean-Square (LMS), Normalized Least-Mean-Square (NLMS) and
Recursive Least-Squares (RLS) adaptive filtering are conducted to find
the best solution for the speaker verification system.
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