Abstract
Various speech enhancement Algorithms (SEA) have been developed in the last few
decades. Each algorithm has its advantages and disadvantages because the speech signal is
affected by environmental situations. Distortion of speech results in the loss of important
features that make this signal challenging to understand. SEA aims to improve the
intelligibility and quality of speech that different types of noise have degraded. In most
applications, quality improvement is highly desirable as it can reduce listener fatigue,
especially when the listener is exposed to high noise levels for extended periods (e.g.,
manufacturing). SEA reduces or suppresses the background noise to some degree,
sometimes called noise suppression algorithms. In this research, the design of SEA based on
different speech models (Laplacian model or Gaussian model) has been implemented using
two types of discrete transforms, which are Discrete Tchebichef Transform and Discrete
Tchebichef-Krawtchouk Transforms. The proposed estimator consists of dual stages of a
wiener filter that can effectively estimate the clean speech signal. The evaluation measures'
results show the proposed SEA's ability to enhance the noisy speech signal based on a
comparison with other types of speech models and a self-comparison based on different
types and levels of noise. The presented algorithm's improvements ratio regarding the
average SNRseq are 1.96, 2.12, and 2.03 for Buccaneer, White, and Pink noise, respectively.
decades. Each algorithm has its advantages and disadvantages because the speech signal is
affected by environmental situations. Distortion of speech results in the loss of important
features that make this signal challenging to understand. SEA aims to improve the
intelligibility and quality of speech that different types of noise have degraded. In most
applications, quality improvement is highly desirable as it can reduce listener fatigue,
especially when the listener is exposed to high noise levels for extended periods (e.g.,
manufacturing). SEA reduces or suppresses the background noise to some degree,
sometimes called noise suppression algorithms. In this research, the design of SEA based on
different speech models (Laplacian model or Gaussian model) has been implemented using
two types of discrete transforms, which are Discrete Tchebichef Transform and Discrete
Tchebichef-Krawtchouk Transforms. The proposed estimator consists of dual stages of a
wiener filter that can effectively estimate the clean speech signal. The evaluation measures'
results show the proposed SEA's ability to enhance the noisy speech signal based on a
comparison with other types of speech models and a self-comparison based on different
types and levels of noise. The presented algorithm's improvements ratio regarding the
average SNRseq are 1.96, 2.12, and 2.03 for Buccaneer, White, and Pink noise, respectively.
Keywords
Discrete Tchebichef Transform (DTT)
Discrete Tchebichef-Krawtchouk Transform (DTKT).
Gaussian speech model
Laplacian speech model
Speech Enhancement Algorithms (SEA)