Modified least mean square adaptive filter for speech enhancement

Kalamani, M.; Makkithaya, Krishnamoorthi

doi:10.1016/b978-0-12-823898-1.00004-7

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…A Modified Least Mean Squares Adaptive Noise Reduction (LMS-ANR) algorithm to improve Tamil speech signal in non-stationary noise environments to an acceptable quality is developed [15]. Automatic speech recognition (ASR) is an effective technique that can convert human speech into text format or computer actions.…”

Section: Related Workmentioning

confidence: 99%

Investigation of the Effectiveness of Audio Processing and Filtering Strategies in Noisy Environments on Speech Recognition Performance

ÖZKURT

2024

Preprint

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This study investigates the effects of audio processing and filtering strategies to enhance the performance of speech recognition systems in noisy environments. The focus is on the Short-Time Fourier Transform (STFT) operations applied to noisy audio files and noise reduction procedures. While STFT operations form the basis for detecting noise and analyzing the speech signal in the frequency domain, noise reduction steps involve threshold-based masking and convolution operations. The results obtained demonstrate a significant improvement in speech recognition accuracy in noisy environments through audio processing and filtering strategies. A detailed analysis of the graphs provides guidance for evaluating the effectiveness of noise reduction procedures and serves as a roadmap for future research. This study emphasizes the critical importance of audio processing and filtering strategies in improving the performance of speech recognition systems in noisy environments, laying a foundation for future studies.

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Section: Related Workmentioning

confidence: 99%