Previsão  De  Consumo  De  Energia  Utilizando  Rede Neural Com Retardo De Tempo (Tdnn)

Bonfim, Bruno Parpinelli; Bratifich, Rafael; Silva, Marcelo Marques da; Silva, Hugo Gomes

doi:10.5747/ce.2020.v12.n4.e340

Cited by 1 publication

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“…Experimental results show that its word error rate is large and model size is greatly reduced, and speech recognition performance is improved [9]. [1] used TDNN to predict the active power demand on a P4 bus in President Prudente. Experimental results demonstrated its validity [11].…”

Section: Introductionmentioning

confidence: 99%

A Study of Blockchain and Machine Learning-Enabled IoT Security in Time-Delayed Neural Network Vocal Pattern Recognition to Improve Web-Based Vocal Teaching

Long

2024

SCPE

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With the development of information technology, online vocal teaching is becoming more and more popular, but the sound quality of teaching is also becoming more and more demanding. As online vocal instruction becomes more popular, the need for high-quality sound in these digital environments becomes more critical. This research tackles the problem of improving sound quality in real-time vocal teaching by integrating advanced technologies such as Blockchain and Machine Learning within the Internet of Things (IoT) security framework. We created a vocal recognition model using Time-Delay Neural Network (TDNN) and improved it with Generated Feature Vector (GFV). This integration yields a strong GTDNN vocal recognition system that is specifically designed to secure and optimize web-based vocal teaching. Our experiments show that GTDNN outperforms traditional TDNN and i-vector methods in feature vector extraction, adapting well to different speech environments. In various speech settings, GTDNN's Error Rates (EERs) are impressively low at 11.3%, 12.0%, 4.9%, 6.2%, and 6.1%, indicating superior performance over comparison models. GTDNN has an EER of 9.6% for short-duration speech and 2.3% for long-duration speech. Furthermore, the GTDNN system achieves an overall pass rate of 94% for target speech and an impressive rejection rate for non-target speech, ensuring high accuracy in a variety of speech environments.

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Section: Introductionmentioning

confidence: 99%

A Study of Blockchain and Machine Learning-Enabled IoT Security in Time-Delayed Neural Network Vocal Pattern Recognition to Improve Web-Based Vocal Teaching

Long

2024

SCPE

View full text Add to dashboard Cite

show abstract

Previsão De Consumo De Energia Utilizando Rede Neural Com Retardo De Tempo (Tdnn)

Cited by 1 publication

References 3 publications

A Study of Blockchain and Machine Learning-Enabled IoT Security in Time-Delayed Neural Network Vocal Pattern Recognition to Improve Web-Based Vocal Teaching

A Study of Blockchain and Machine Learning-Enabled IoT Security in Time-Delayed Neural Network Vocal Pattern Recognition to Improve Web-Based Vocal Teaching

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