Open AccessIntelligent Assessment of 95598 Speech Transcription Text Quality Based on Topic Model
Author(s) -
Bochuan Song,
Peng Wu,
Qiang Zhang,
Bo Chai,
Yuanbo Gao,
Yulin He
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/563/4/042001
Subject(s) - computer science , latent dirichlet allocation , topic model , cluster analysis , natural language processing , preprocessor , artificial intelligence , transcription (linguistics) , data pre processing , information retrieval , hidden markov model , data mining , text processing , field (mathematics) , speech recognition , linguistics , philosophy , mathematics , pure mathematics
The quality of speech transcripts is of great significance to power system management and is an important basis for supporting subsequent data analysis. In this paper, based on the characteristics of speech transcription texts of customer service, this paper proposes an analysis method combining manual processing and latent Dirichlet allocation topic model, analyzing the transcribed texts. First, data preprocessing is performed on the State Grid’s work order data, and then the text topic distribution calculation is performed by the LDA topic model, and the topic parameter is set to a total of 100 topics. Next, the unsupervised clustering of the documents is performed by the k-means method, and the similarity between the files is obtained. Finally, the quality of the data is analyzed by combining manual labeling and manual evaluation. For the first time, this paper marks and identifies the State Grid’s work order analysis data, which is a pioneering work for natural language processing technology in the field of power grid.