Comparison of movement density and destination cluster analysis of residents and tourists using Jeju Island navigation data before and after the COVID-19

Um, Taehyee; Koh, Sun-Young; Chung, Namho

doi:10.37272/jiecr.2021.04.21.2.35

Cited by 1 publication

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“…e objective methods commonly used to evaluate the performance of speech recognition models include word error rate, sentence error rate, and character error rate [28]. e experiments test the model performance by taking the word error rate, which is the error rate for phoneme recognition in English speech recognition.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Hybrid Algorithm for English Translation Speech Recognition Based on Deep Learning Model and Clustering

Zhang

2022

Security and Communication Networks

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Speech recognition is the most important research direction in human-computer interaction. It is the key to the connection between human beings and machines and the expression of intelligence and automation in the information society. Taking English as the research object, using the related knowledge of speech recognition, it is based on the hidden Markov model technology of deep learning and clustering analysis algorithm and evaluated according to the cross-language English phonemic recognition system of sparse autoencoder (SA) method. By studying the speech recognition algorithm of the English translation, the influence of the speech recognition environment on the accuracy of speech recognition is confirmed. This provides a direction for humans to study speech recognition at a deeper level. Based on the language model of Transformer and the language model based on Seq2Seq, it sets different vocabularies, and the data are collected in the laboratory and outdoors, respectively, and the posttest template library is formed after collection. In the task of restoring phonetic symbols to English characters when phonemes are modeling units, the error rate is the lowest. The error rate on the test set reached 9.54%, which was 6.97 percentage points higher than that of the syllable modeling unit.

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Section: Experiments and Resultsmentioning

confidence: 99%

Hybrid Algorithm for English Translation Speech Recognition Based on Deep Learning Model and Clustering

Zhang

2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

Comparison of movement density and destination cluster analysis of residents and tourists using Jeju Island navigation data before and after the COVID-19

Cited by 1 publication

References 0 publications

Hybrid Algorithm for English Translation Speech Recognition Based on Deep Learning Model and Clustering

Hybrid Algorithm for English Translation Speech Recognition Based on Deep Learning Model and Clustering

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