ArticleSensors (Basel, Switzerland)2022
Improved Feature Parameter Extraction from Speech Signals Using Machine Learning Algorithm.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
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- Feature selection for emotion recognition in speech: a comparative study of filter and wrapper methods.PeerJ. Computer science · 2025Article
- Assessment of Pepper Robot's Speech Recognition System through the Lens of Machine Learning.Biomimetics (Basel, Switzerland) · 2024Article
- Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments.Bioengineering (Basel, Switzerland) · 2024Article
- Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches.Sensors (Basel, Switzerland) · 2023Article
- Failure Severity Prediction for Protective-Coating Disbondment via the Classification of Acoustic Emission Signals.Sensors (Basel, Switzerland) · 2023Article
- Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety.Sensors (Basel, Switzerland) · 2023Article
- Multimodal Emotion Detection via Attention-Based Fusion of Extracted Facial and Speech Features.Sensors (Basel, Switzerland) · 2023Article
- Artificial Intelligence for Automatic Monitoring of Respiratory Health Conditions in Smart Swine Farming.Animals : an open access journal from MDPI · 2023Review
- Evaluating Synthetic Medical Images Using Artificial Intelligence with the GAN Algorithm.Sensors (Basel, Switzerland) · 2023Article
- A YOLOv6-Based Improved Fire Detection Approach for Smart City Environments.Sensors (Basel, Switzerland) · 2023Article
- An Improved Forest Fire Detection Method Based on the Detectron2 Model and a Deep Learning Approach.Sensors (Basel, Switzerland) · 2023Article
- Development of Language Models for Continuous Uzbek Speech Recognition System.Sensors (Basel, Switzerland) · 2023Article
- Improved Face Detection Method via Learning Small Faces on Hard Images Based on a Deep Learning Approach.Sensors (Basel, Switzerland) · 2023Article
- Detecting Lombard Speech Using Deep Learning Approach.Sensors (Basel, Switzerland) · 2022Article
- Improved Agricultural Field Segmentation in Satellite Imagery Using TL-ResUNet Architecture.Sensors (Basel, Switzerland) · 2022Article
- Development of Real-Time Landmark-Based Emotion Recognition CNN for Masked Faces.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Speech recognition refers to the capability of software or hardware to receive a speech signal, identify the speaker's features in the speech signal, and recognize the speaker thereafter. In general, the speech recognition process involves three main steps: acoustic processing, feature extraction, and classification/recognition. The purpose of feature extraction is to illustrate a speech signal using a predetermined number of signal components. This is because all information in the acoustic signal is excessively cumbersome to handle, and some information is irrelevant in the identification task. This study proposes a machine learning-based approach that performs feature parameter extraction from speech signals to improve the performance of speech recognition applications in real-time smart city environments. Moreover, the principle of mapping a block of main memory to the cache is used efficiently to reduce computing time. The block size of cache memory is a parameter that strongly affects the cache performance. In particular, the implementation of such processes in real-time systems requires a high computation speed. Processing speed plays an important role in speech recognition in real-time systems. It requires the use of modern technologies and fast algorithms that increase the acceleration in extracting the feature parameters from speech signals. Problems with overclocking during the digital processing of speech signals have yet to be completely resolved. The experimental results demonstrate that the proposed method successfully extracts the signal features and achieves seamless classification performance compared to other conventional speech recognition algorithms.
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