Parametric multi-band automatic gain control for noisy speech enhancement

Stolbov, Mikhail; Koval, Serguei; Khitrov, Mikhail

doi:10.21437/eurospeech.2003-226

Cited by 1 publication

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“…As shown in the lower part of Figure 1, the function unit of feature extraction is in front of the recognition. Moreover, gain controls are widely integrated to realize functions, such as adjusting the signal strength [7,17,27], improving the perceptual intelligibility [8,9], optimizing the ASR performance directly [28], or by speech enhancement [18,19,39,40].…”

Section: Related Workmentioning

confidence: 99%

Automatic Speech Recognition Performance Improvement for Mandarin Based on Optimizing Gain Control Strategy

Wang

Wei

Zhang

et al. 2022

Sensors

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Automatic speech recognition (ASR) is an essential technique of human–computer interactions; gain control is a commonly used operation in ASR. However, inappropriate gain control strategies can lead to an increase in the word error rate (WER) of ASR. As there is a current lack of sufficient theoretical analyses and proof of the relationship between gain control and WER, various unconstrained gain control strategies have been adopted on realistic ASR systems, and the optimal gain control with respect to the lowest WER, is rarely achieved. A gain control strategy named maximized original signal transmission (MOST) is proposed in this study to minimize the adverse impact of gain control on ASR systems. First, by modeling the gain control strategy, the quantitative relationship between the gain control strategy and the ASR performance was established using the noise figure index. Second, through an analysis of the quantitative relationship, an optimal MOST gain control strategy with minimal performance degradation was theoretically deduced. Finally, comprehensive comparative experiments on a Mandarin dataset show that the proposed MOST gain control strategy can significantly reduce the WER of the experimental ASR system, with a 10% mean absolute WER reduction at −9 dB gain.

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