Key result
Linear hard shrinkage outperforms other models in denoising deaf speech signals.
Why the study?
Denoising of speech signals is essential to improve the fidelity and understanding of deaf speech, which is complex and affected by noise.
Answers are grounded in this paper and cite their sources.
May aid speech processing pipelines; leaves open clinical validation in audiology.
Speech is vital parameter in human cognitive and socio-emotional development. Deaf speech is even more complex to understand unless the noise is removed and improved its fidelity. Denoising of speech signal is an important pre-processing stage in speech signal processing. Denoising of speech signal not only removes the redundant coefficients (leads to data compression) but also improve the quality of speech signal. Waveshrink is a technique used to in this paper to suppress the redundant wavelet coefficient through threshold concept. Denoising concept find application in both signal and image processing. Denoising of the signal will improve in processing the deaf signal speech with any assistive technologies. Digital devices can use this denoised speech to compare and reproduce the deaf speech signal for emotional analysis and comparison of various speech parameters in real time environment.
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Nirmaladevi et al. (2022) studied Deaf speech (n=10). WaveShrink Method (Hard shrinkage) vs. Soft, Negative Garrot, and Hyper Shrinkage functions was evaluated on Signal to Noise Ratio (SNR) and Percent RMS difference (PRD). The linear hard shrinkage model outperformed other shrinkage models in denoising deaf speech signals, achieving higher Signal to Noise Ratio and better Percent RMS difference.
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