Mitigate the Reverberant Effects on Speaker Recognition via Multi-training

Mohammed, Duraid Y.; Al-Karawi, Khamis A.; Husien, Idress Mohammed; Ghulam, Marwah Abdullah

doi:10.1007/978-3-030-38752-5_8

Cited by 11 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The IoT significantly benefits individuals with disabilities, enhancing mobility, communication, and functionality. Smart home systems and customized assistive devices improve the quality of life, enhancing independence (Shahrestani, 2017;Mohammed et al, 2020). IoT devices are revolutionizing health monitoring, navigation, education, and employment, promoting accessibility, customization, and social inclusion, and improving independence and quality of life for disabled individuals (Farahani et al, 2020;Abdelwahab et al, 2024).…”

Section: Advantages Of Iot For Individuals With Disabilitiesmentioning

confidence: 99%

A Review on Internet of Things (IoT)-Related Disabilities and Their Implications

Semary,

Al-Karawi,

Abdelwahab

et al. 2024

Journal of Disability Research

View full text Add to dashboard Cite

The transformative potential of the Internet of Things (IoT) extends to the lives of approximately one billion individuals worldwide living with disabilities, representing around 15% of the global population. This article highlights the importance of addressing challenges related to disability inclusivity and support, recognizing their significant impact on individuals and the global community. Despite inadequate support services, the IoT emerges as a beacon of hope, offering innovative solutions to empower people with disabilities. By integrating smart devices and technologies, the IoT can disrupt the cycle of dependence on families, thereby fostering economic activity and social inclusion. Introducing a proposed IoT framework elucidates its pivotal role in enhancing the quality of life for individuals with disabilities and facilitating their active engagement in societal and economic spheres. The article explores diverse application scenarios to underscore how the IoT can meet the unique needs of this demographic. Furthermore, it addresses critical challenges that must be acknowledged and overcome to implement IoT-based solutions in this context successfully. This study offers a comprehensive overview of IoT solutions tailored to mitigate the challenges encountered by individuals grappling with visual impairments.

show abstract

Section: Advantages Of Iot For Individuals With Disabilitiesmentioning

confidence: 99%

A Review on Internet of Things (IoT)-Related Disabilities and Their Implications

Semary,

Al-Karawi,

Abdelwahab

et al. 2024

Journal of Disability Research

View full text Add to dashboard Cite

show abstract

“…This solution is simple, computationally efficient, accessible, reliable, and adaptable to any platform, reducing computational burden and allowing for customization (Mohammed et al, 2021;Alenizi and Al-Karawi, 2023a). Our ASR system aims to provide a cost-effective, reliable, and accessible solution for computational resources, promoting openness, collaboration, and innovation in speech recognition, by eliminating the limitations of existing commercial systems (Mohammed et al, 2020;Al-Karawi, 2023). Our ASR development approach offers flexibility in system improvements, customization, and exploration, enhancing speech recognition technology accessibility, affordability, and adaptability for various applications.…”

Section: Introductionmentioning

confidence: 99%

Using Voice Technologies to Support Disabled People

Semary,

Al-Karawi,

Abdelwahab

2024

Journal of Disability Research

Self Cite

View full text Add to dashboard Cite

In recent years, significant strides have been made in speech and speaker recognition systems, owing to the rapid evolution of data processing capabilities. Utilizing a speech recognition system facilitates straightforward and efficient interaction, especially for individuals with disabilities. This article introduces an automatic speech recognition (ASR) system designed for seamless adaptation across diverse platforms. The model is meticulously described, emphasizing clarity and detail to ensure reproducibility for researchers advancing in this field. The model’s architecture encompasses four stages: data acquisition, preprocessing, feature extraction, and pattern recognition. Comprehensive insights into the system’s functionality are provided in the Experiments and Results section. In this study, an ASR system is introduced as a valuable addition to the advancement of educational platforms, enhancing accessibility for individuals with visual disabilities. While the achieved recognition accuracy levels are promising, they may not match those of certain commercial systems. Nevertheless, the proposed model offers a cost-effective solution with low computational requirements. It seamlessly integrates with various platforms, facilitates straightforward modifications for developers, and can be tailored to the specific needs of individual users. Additionally, the system allows for the effortless inclusion of new words in its database through a single recording process.

show abstract

“…Each speaker sample has been convoluted with different noisy conditions. During the recognition phase, the reference model which is closest to the features of the input speech sample is then selected [8], [9]. Zhao et al has used spectral energy calculated through short segments has been as dominant features to discriminate the speech samples from other soundtracks [10].…”

Section: Introductionmentioning

confidence: 99%

Robust speaker verification by combining MFCC and entrocy in noisy conditions

2021

Self Cite

View full text Add to dashboard Cite

Automatic speaker recognition may achieve remarkable performance in matched training and test conditions. Conversely, results drop significantly in incompatible noisy conditions. Furthermore, feature extraction significantly affects performance. Mel-frequency cepstral coefficients MFCCs are most commonly used in this field of study. The literature has reported that the conditions for training and testing are highly correlated. Taken together, these facts support strong recommendations for using MFCC features in similar environmental conditions (train/test) for speaker recognition. However, with noise and reverberation present, MFCC performance is not reliable. To address this, we propose a new feature 'entrocy' for accurate and robust speaker recognition, which we mainly employ to support MFCC coefficients in noisy environments. Entrocy is the fourier transform of the entropy, a measure of the fluctuation of the information in sound segments over time. Entrocy features are combined with MFCCs to generate a composite feature set which is tested using the gaussian mixture model (GMM) speaker recognition method. The proposed method shows improved recognition accuracy over a range of signal-to-noise ratios.

show abstract

Mitigate the Reverberant Effects on Speaker Recognition via Multi-training

Cited by 11 publications

References 17 publications

A Review on Internet of Things (IoT)-Related Disabilities and Their Implications

A Review on Internet of Things (IoT)-Related Disabilities and Their Implications

Using Voice Technologies to Support Disabled People

Robust speaker verification by combining MFCC and entrocy in noisy conditions

Contact Info

Product

Resources

About