Wearable Speech Enhancement System for Motor Impaired People

Palla, Alessandro; Fanucci, Luca; Sannino, Roberto; Settin, Mattia

doi:10.1007/978-3-319-47913-2_19

Cited by 3 publications

(11 citation statements)

References 3 publications

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“…Wearable platform, where the speech enhancing process is performed, and 2. Computing platform where the word recognition accuracy process is carried out [3].…”

Section: Wearable Speech Enhancement (Wse) Systemmentioning

confidence: 99%

“…As we can use wearable devices in a noisy environment, the speech application systems such as ASR needs to eliminate the noise to improve their performance and robustness. Wearable devices were used in speech enhancement due to the size and proximity with the mouth that reduces the noise while recording the speech [2], [3]. For example, suppose everybody always wears a personal microphone near his/her mouth; the signal-to-noise ratio can be vastly improved, compared with having a microphone at a greater distance [3].…”

Section: Introductionmentioning

confidence: 99%

“…Wearable devices were used in speech enhancement due to the size and proximity with the mouth that reduces the noise while recording the speech [2], [3]. For example, suppose everybody always wears a personal microphone near his/her mouth; the signal-to-noise ratio can be vastly improved, compared with having a microphone at a greater distance [3]. Most hearing devices, such as…”

Section: Introductionmentioning

confidence: 99%

“…The wearable microphone array, embedded in textiles, consists of multiple microphones used to record the speech signal that gives the best recognition at 10dB SNR than a single microphone [2]. Microphone array and speech enhancement are the components embedded in wearable speech enhancement (WSE) that process speech signals in multichannel under noisy environments such as the outdoor environments [3]. For example, a spectral statistical filter is applied in wearable hearing devices for handling stationary noise environment (Gaussian noise) and non-stationary noise environments (babble, factory, and car) at −5dB to 20dB [4].…”

Section: Introductionmentioning

confidence: 99%

“…Although the existing system developed for WSE in noisy environments was only tested for White Gaussian noise (WGN) at 0 to 60dB SNR (which is stationary noise) [5], [3], they were never tested for non-stationary environmental noises such as Airport noise, babble noise, car noise, Exhibition noise, and Restaurant noise.…”

Section: Introductionmentioning

confidence: 99%

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The Performance of Wearable Speech Enhancement System Under Noisy Environment: An Experimental Study

2022

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Wearable speech enhancement can improve the recognition accuracy of the speech signals in stationary noise environments at 0dB to 60dB signal to noise ratio. Beamforming, adaptive noise reduction, and voice activity detection algorithms are used in wearable speech enhancement systems to enhance speech signals. In recent works, a word rate recognition accuracy of 63% for a 0db signal-to-noise ratio is not satisfactory for a robust speech recognition system. This paper discusses the experimental study using fixed beamforming, adaptive noise reduction, and voice activity detection algorithms with the inclusion of −10dB to 20dB signal to noise ratio for different types of noises to test the wearable speech enhancement system's performance in noisy environments. It also compares deep learning-based noise reduction methods as a benchmark for speech enhancement and word recognition for different noise levels. We have obtained an average word rate recognition accuracy of 5.74% at −10dB and 93.79% at 20dB for non-stationary noisy environments. The outcome of the experiments shows that the selected methods perform significantly better in the environment with high noise dB for both stationary and non-stationary noise. We found that there is no significant statistical difference between the stationary and non-stationary noise word recognition and SNRs level. However, the deep learning-based method performs significantly better than the fixed beamforming, adaptive noise reduction, and voice activity detection algorithms in all noisy levels.

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“…Wearable platform, where the speech enhancing process is performed, and 2. Computing platform where the word recognition accuracy process is carried out [3].…”

Section: Wearable Speech Enhancement (Wse) Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Performance of Wearable Speech Enhancement System Under Noisy Environment: An Experimental Study

2022

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Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0

et al. 2022

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The Industry 4.0 initiative has been showing the way for industrial production to optimize operations based on collecting, processing, and sharing data. There are new requirements on the production floor: flexible but ultra-reliable, low latency wireless communications through interoperable systems can share data. Further challenges of data sharing and storage arise when diverse systems come into play at the Manufacturing Operations Management and Business Planning & Logistics levels. The emerging complex cyber-physical systems of systems need to be engineered with care. Regarding industrial requirements, the telecommunication industry has many similarities to production—including ultra-reliability, high complexity, and having humans “in-the-loop”. The current paper aims to provide an overview of converging telco-grade solutions that can be successfully applied in the wide sense of industrial production. These toolsets range from model-driven engineering through system interoperability frameworks, 5G- and 6G-supported manufacturing, and the telco-cloud to speech recognition in noisy environments.

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CNN-based noise reduction for multi-channel speech enhancement system with discrete wavelet transform (DWT) preprocessing

Cherukuru,

Mustafa

2024

PeerJ Computer Science

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Speech enhancement algorithms are applied in multiple levels of enhancement to improve the quality of speech signals under noisy environments known as multi-channel speech enhancement (MCSE) systems. Numerous existing algorithms are used to filter noise in speech enhancement systems, which are typically employed as a pre-processor to reduce noise and improve speech quality. They may, however, be limited in performing well under low signal-to-noise ratio (SNR) situations. The speech devices are exposed to all kinds of environmental noises which may go up to a high-level frequency of noises. The objective of this research is to conduct a noise reduction experiment for a multi-channel speech enhancement (MCSE) system in stationary and non-stationary environmental noisy situations with varying speech signal SNR levels. The experiments examined the performance of the existing and the proposed MCSE systems for environmental noises in filtering low to high SNRs environmental noises (−10 dB to 20 dB). The experiments were conducted using the AURORA and LibriSpeech datasets, which consist of different types of environmental noises. The existing MCSE (BAV-MCSE) makes use of beamforming, adaptive noise reduction and voice activity detection algorithms (BAV) to filter the noises from speech signals. The proposed MCSE (DWT-CNN-MCSE) system was developed based on discrete wavelet transform (DWT) preprocessing and convolution neural network (CNN) for denoising the input noisy speech signals to improve the performance accuracy. The performance of the existing BAV-MCSE and the proposed DWT-CNN-MCSE were measured using spectrogram analysis and word recognition rate (WRR). It was identified that the existing BAV-MCSE reported the highest WRR at 93.77% for a high SNR (at 20 dB) and 5.64% on average for a low SNR (at −10 dB) for different noises. The proposed DWT-CNN-MCSE system has proven to perform well at a low SNR with WRR of 70.55% and the highest improvement (64.91% WRR) at −10 dB SNR.

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Wearable Speech Enhancement System for Motor Impaired People

Cited by 3 publications

References 3 publications

The Performance of Wearable Speech Enhancement System Under Noisy Environment: An Experimental Study

The Performance of Wearable Speech Enhancement System Under Noisy Environment: An Experimental Study

Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0

CNN-based noise reduction for multi-channel speech enhancement system with discrete wavelet transform (DWT) preprocessing

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