Title: Neural Network Based Speaker Classification and Verification Systems with Enhanced Features,
Abstract: This work presents a novel framework based on feed-forward neural network for
text-independent speaker classification and verification, two related systems
of speaker recognition. With optimized features and model training, it achieves
100% classification rate in classification and less than 6% Equal Error Rate
(ERR), using merely about 1 second and 5 seconds of data respectively. Features
with stricter Voice Active Detection (VAD) than the regular one for speech
recognition ensure extracting stronger voiced portion for speaker recognition,
speaker-level mean and variance normalization helps to eliminate the
discrepancy between samples from the same speaker. Both are proven to improve
the system performance. In building the neural network speaker classifier, the
network structure parameters are optimized with grid search and dynamically
reduced regularization parameters are used to avoid training terminated in
local minimum. It enables the training goes further with lower cost. In speaker
verification, performance is improved with prediction score normalization,
which rewards the speaker identity indices with distinct peaks and penalizes
the weak ones with high scores but more competitors, and speaker-specific
thresholding, which significantly reduces ERR in the ROC curve. TIMIT corpus
with 8K sampling rate is used here. First 200 male speakers are used to train
and test the classification performance. The testing files of them are used as
in-domain registered speakers, while data from the remaining 126 male speakers
are used as out-of-domain speakers, i.e. imposters in speaker verification.