Abstract:
In the last decade, speech-features have been effectively
utilized for estimating cognitive load level in ideal conditions
where recorded speech is clean. However, in more realistic
conditions, the recorded speech data is corrupted by noise. Hence,
the employment of speech enhancement is essential to reduce the
noise. In this paper, the effectiveness of three speech enhancement
algorithms proposed in our previous studies are compared based
on performance and processing time and the most suitable
method is utilized to denoise the input noisy speech before feeding
it to a cognitive load classification system in order to improve its
performance. The results of this study indicate that the use of
speech enhancement can reduce 3.0% of average relative error
rate for the system under the effect of various noisy conditions.
