Performance analysis of adaptive wavelet denosing by speech discrimination and thresholding

Mahadevaswamy,; Ravì, Daniele

doi:10.1109/iceeccot.2016.7955209

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Discrete Wavelet Transform (Cwt)1

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2021

2024

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Cited by 7 publications

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“…The CWT is more complicated for signal analysis, because it involves significant computational resources. While DWT is less complicated in capture the signal information effectively [49]. The DWT of signal x(t) is defined as:…”

Section: Discrete Wavelet Transform (Cwt)mentioning

confidence: 99%

Robust Perceptual Wavelet Packet Features for Recognition of Continuous Kannada Speech

Mahadevaswamy

Ravì

2021

Wireless Pers Commun

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An ASR system is built for the Continuous Kannada Speech Recognition. The acoustic and language models are created with the help of the Kaldi toolkit. The speech database is created with the native male and female Kannada speakers. The 75% of collected speech data is used for training the acoustic models and 25% of speech database is used for the system testing. The Performance of the system is presented interms of Word Error Rate (WER). Wavelet Packet Decomposition along with Mel filter bank is used to achieve feature extraction. The proposed feature extraction performs slightly better than the conventional features such as MFCC, PLP interms of WRA and WER under uncontrolled conditions. For the speech corpus collected in Kannada Language, theproposed features shows an improvement in WRA of 1.79% over baseline features.

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Section: Discrete Wavelet Transform (Cwt)mentioning

confidence: 99%