Performance analysis of speech enhancement methods using adaptive algorithms and optimization techniques

Anoop, V

doi:10.1109/iccsp.2015.7322720

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…First, speech frames are decomposed into three groups of speech models: amplitude, frequency, and phase [24]. The input speech frequency above the reference cut-off frequency (fc) was reallocated towards a lower frequency range to improve high-frequency speech recognition ability [25]. Frequency compression ratio (CR) was set for various frequency ranges.…”

Section: Literature Reviewmentioning

confidence: 99%

Marathi Speech Intelligibility Enhancement Using I-AMS Based Neuro-Fuzzy Classifier Approach for Hearing Aid Users

et al. 2022

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Globally, 1.6 billion individuals suffered from hearing disability in 2019. According to the World Health Organization, by 2050, the number of people with hearing impairments will rise to 2.5 billion. Speech perception in noisy surroundings is a challenge for hearing aid users. This study aimed to design a novel methodology to improve the speech recognition ability of hearing aid users from various backgrounds. To improve speech enhancement, we propose a discrete cosine transform (DCT)-based improved amplitude-magnitude spectrogram (I-AMS) algorithm with a fuzzy classifier. First, the I-AMS approach disintegrates speech signals containing noise into time-frequency units and eliminates the noise present in the signal. Next, the time frequency units (t-f units), modulation frequency (fm), and centre frequency (fc) are extracted from the denoised signal. A neuro-fuzzy classifier was used to classify the background speech environment into three different classes. The proposed I-AMS algorithm was tested, achieved improvements in terms of sensitivity (+1.02%) and accuracy (+11.80%). Speech denoising revealed a 1.27% improvement in speech recognition performance.

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Section: Literature Reviewmentioning

confidence: 99%

Marathi Speech Intelligibility Enhancement Using I-AMS Based Neuro-Fuzzy Classifier Approach for Hearing Aid Users

et al. 2022

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“…Then the resultant speech is enhanced by projecting it into the speech subspace [6]. Also different adaptive algorithms have been used for enhancement application [7][8]. Speech quality might be degraded in the presence of background noise.…”

Section: Introductionmentioning

confidence: 99%