A speech to machine interface based on the frequency domain command recognition

Almayouf, Nojood; Qaisar, Saeed Mian; Alharbi, Lojain; Madani, Raghdah

doi:10.1109/siprocess.2017.8124564

Cited by 2 publications

(2 citation statements)

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“…Its purpose is to assist and provide help for the purpose of meeting the needs of elderly and disabled people. Reference [13] indicated that recent technological advances allow the development of voicemachine interfaces, systems that can be used in a variety of potential applications, such as security, smart homes, and centers for people with disabilities. The proposal focused on a frequency domain speech recognition platform, where a spectral analysis of speech to extract its characteristics was performed.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Human Actions through Speech or Voice Using Machine Learning Techniques

Peña-Cáceres,

Silva-Marchan,

Albert

et al. 2023

CMC

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The development of artificial intelligence (AI) and smart home technologies has driven the need for speech recognition-based solutions. This demand stems from the quest for more intuitive and natural interaction between users and smart devices in their homes. Speech recognition allows users to control devices and perform everyday actions through spoken commands, eliminating the need for physical interfaces or touch screens and enabling specific tasks such as turning on or off the light, heating, or lowering the blinds. The purpose of this study is to develop a speech-based classification model for recognizing human actions in the smart home. It seeks to demonstrate the effectiveness and feasibility of using machine learning techniques in predicting categories, subcategories, and actions from sentences. A dataset labeled with relevant information about categories, subcategories, and actions related to human actions in the smart home is used. The methodology uses machine learning techniques implemented in Python, extracting features using CountVectorizer to convert sentences into numerical representations. The results show that the classification model is able to accurately predict categories, subcategories, and actions based on sentences, with 82.99% accuracy for category, 76.19% accuracy for subcategory, and 90.28% accuracy for action. The study concludes that using machine learning techniques is effective for recognizing and classifying human actions in the smart home, supporting its feasibility in various scenarios and opening new possibilities for advanced natural language processing systems in the field of AI and smart homes.

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Section: Related Workmentioning

confidence: 99%

Recognition of Human Actions through Speech or Voice Using Machine Learning Techniques

Peña-Cáceres,

Silva-Marchan,

Albert

et al. 2023

CMC

View full text Add to dashboard Cite

show abstract

“…It is achieved by employing an appropriate binary weighted length Fast Fourier Transform (FFT) [34]. It allows exploiting the signal spectra for extracting valuable information [35].…”

Section: A/d Conversionmentioning

confidence: 99%

Speech Recognition and a Cymatics Based Configurable Speech Perception

Qaisar¹

2018

Preprint

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This paper propose an original approach of achieving a Cymatics based visual perception of isolated speech commands. The idea is to smartly combine the effective speech processing and analysis methods with the phenomena of Cymatics. In this context, an effective approach for automatic isolated speech based message recognition is proposed. The incoming speech segment is enhanced by applying the appropriate pre-emphasis filtering, noise thresholding and zero alignment operations. The Mel-Frequency Cepstral coefficients (MFCCs), Delta coefficients and Delta-Delta coefficients are extracted from the enhanced speech segment. Later on, the Dynamic Time Warping (DTW) technique is employed to compare these extracted features with the reference templates. The comparison outcomes are used to make the classification decision. The classification decision is transformed into a methodical excitation. Finally, this excitation is converted into the systematic visual perceptions via the phenomenon of Cymatics. The system functionality is tested with an experimental setup and results are presented. The approach is novel and can be employed in various applications like visual art, encryption, education, archeology, architecture, integration of impaired people, etc.

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A speech to machine interface based on the frequency domain command recognition

Cited by 2 publications

References 6 publications

Recognition of Human Actions through Speech or Voice Using Machine Learning Techniques

Recognition of Human Actions through Speech or Voice Using Machine Learning Techniques

Speech Recognition and a Cymatics Based Configurable Speech Perception

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