Implementation of Mel Frequency Cepstrum Coefficients and Dynamic Time Warping Algorithms on Door Physical Security System using Voice Recognition Pattern

Yolanda, Desta; Hersyah, Mohammad Hafiz; Pratama, Rizky Nugraha

doi:10.1109/icitsi50517.2020.9264907

Cited by 4 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nyquist's theorem states that the reconstruction of the original signal is possible if it is sampled at rates, which is two times greater in frequency available in signals. Yolanda, Hersyah, and Pratama [14] have demonstrated that a sample frequency of approximately 16,000 samples in every second is best for the purpose of voice recognition. Subsequently, a sequence of numerical values will be presented, denoting the magnitude of the acoustic waveform at intervals of 1/16000 seconds.…”

Section: Connectionist Temporal Classificationmentioning

confidence: 99%

A Review of Textual and Voice Processing Algorithms in the Field of Natural Language Processing

Bowden

2023

JCNS

View full text Add to dashboard Cite

Currently, there is a significant focus on natural language processing (NLP) within academic circles. As one of the initial domains of inquiry in the domain of machine learning, it has been utilized in a variety of significant sub-disciplines, such as text processing, speech recognition, and machine translation. Natural language processing has contributed to notable progress in computing and artificial intelligence. The recurrent neural network serves as a fundamental component for numerous techniques in domain of NLP. The present article conducts a comprehensive evaluation of various algorithms for processing textual and voice data, accompanied by illustrative instances of their functionality. Various algorithmic outcomes exhibit the advancements achieved in this field during the preceding decade. Our endeavor involved the classification of algorithms based on their respective types and expounding on the scope for future research in this domain. Furthermore, the study elucidates the potential applications of these heterogeneous algorithms and also evaluates the disparities among them through an analysis of the findings. Despite the fact that natural language processing has not yet achieved its ultimate objective of flawlessness, it is plausible that with sufficient exertion, the field will eventually attain it. Currently, a wide variety of artificial intelligence systems use natural language processing algorithms to comprehend human-spoken directions.

show abstract

Section: Connectionist Temporal Classificationmentioning

confidence: 99%

A Review of Textual and Voice Processing Algorithms in the Field of Natural Language Processing

Bowden

2023

JCNS

View full text Add to dashboard Cite

show abstract

Novel interactive model for voice recognition for machine assisted English training system

Shi

2021

2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA)

View full text Add to dashboard Cite

Children with Speech Disorders Voice Classification: LSTM and BiLSTM Approach Based on MFCC Features

Riyanto,

Rahman,

Istiadi

2023

2023 International Conference on Modeling &Amp;amp; E-Information Research, Artificial Learning and Digital Applications (ICMER

View full text Add to dashboard Cite

Implementation of Mel Frequency Cepstrum Coefficients and Dynamic Time Warping Algorithms on Door Physical Security System using Voice Recognition Pattern

Cited by 4 publications

References 5 publications

A Review of Textual and Voice Processing Algorithms in the Field of Natural Language Processing

A Review of Textual and Voice Processing Algorithms in the Field of Natural Language Processing

Novel interactive model for voice recognition for machine assisted English training system

Children with Speech Disorders Voice Classification: LSTM and BiLSTM Approach Based on MFCC Features

Contact Info

Product

Resources

About