Semantic Scholar Open Access 2022 5 sitasi

Automatic Error Detection Method of Embedded English Speech Teaching Recognition System under the Background of Artificial Intelligence

Wenjuan He

Abstrak

The embedded English speech teaching recognition system is a technology that writes the English speech recognition device control program into the chip and embeds the chip into the device, so that the human-like chip controls the English speech device to complete the speech recognition operation. Applying the embedded technology to the English speech recognition system can improve the recognition accuracy of the system to a higher level in terms of recognizing the English speech of a specific person. The purpose of this paper is to research and design an automatic error detection method for embedded English speech teaching recognition system in the context of artificial intelligence. This paper first gives a general introduction to the overview of artificial intelligence, then analyzes the speech recognition algorithm, uses MatLab software to obtain the correct number of recognition system words and the correct rate, and then implements the embedded English teaching recognition system in different environments. The experimental results of comparison through multiple test analysis show that in a quiet environment, the error rate of the embedded English speech teaching recognition system is very low, and the correct recognition rate can reach more than 90%. In a noisy environment affected by various noises, the correct recognition rate of the embedded English speech teaching recognition system is basically above 60%.

Penulis (1)

W

Wenjuan He

Format Sitasi

He, W. (2022). Automatic Error Detection Method of Embedded English Speech Teaching Recognition System under the Background of Artificial Intelligence. https://doi.org/10.1155/2022/7340051

Akses Cepat

Lihat di Sumber doi.org/10.1155/2022/7340051
Informasi Jurnal
Tahun Terbit
2022
Bahasa
en
Total Sitasi
Sumber Database
Semantic Scholar
DOI
10.1155/2022/7340051
Akses
Open Access ✓