Abstract: In this paper, Fuzzy C-Means clustering with
Expectation Maximization-Gaussian Mixture Model based hybrid
modeling algorithm is proposed for Continuous Tamil Speech
Recognition. The speech sentences from various speakers are used
for training and testing phase and objective measures are between the
proposed and existing Continuous Speech Recognition algorithms.
From the simulated results, it is observed that the proposed algorithm
improves the recognition accuracy and F-measure up to 3% as
compared to that of the existing algorithms for the speech signal from
various speakers. In addition, it reduces the Word Error Rate, Error
Rate and Error up to 4% as compared to that of the existing
algorithms. In all aspects, the proposed hybrid modeling for Tamil
speech recognition provides the significant improvements for speechto-
text conversion in various applications.
Abstract: In this paper, Least Mean Square (LMS) adaptive
noise reduction algorithm is proposed to enhance the speech signal
from the noisy speech. In this, the speech signal is enhanced by
varying the step size as the function of the input signal. Objective and
subjective measures are made under various noises for the proposed
and existing algorithms. From the experimental results, it is seen that
the proposed LMS adaptive noise reduction algorithm reduces Mean
square Error (MSE) and Log Spectral Distance (LSD) as compared to
that of the earlier methods under various noise conditions with
different input SNR levels. In addition, the proposed algorithm
increases the Peak Signal to Noise Ratio (PSNR) and Segmental SNR
improvement (ΔSNRseg) values; improves the Mean Opinion Score
(MOS) as compared to that of the various existing LMS adaptive
noise reduction algorithms. From these experimental results, it is
observed that the proposed LMS adaptive noise reduction algorithm
reduces the speech distortion and residual noise as compared to that
of the existing methods.