A LDA-Based Topic Classification Approach From Highly Imperfect Automatic Transcriptions

Mohamed Morchid, Richard Dufour, Georges Linarès


Abstract
Although the current transcription systems could achieve high recognition performance, they still have a lot of difficulties to transcribe speech in very noisy environments. The transcription quality has a direct impact on classification tasks using text features. In this paper, we propose to identify themes of telephone conversation services with the classical Term Frequency-Inverse Document Frequency using Gini purity criteria (TF-IDF-Gini) method and with a Latent Dirichlet Allocation (LDA) approach. These approaches are coupled with a Support Vector Machine (SVM) classification to resolve theme identification problem. Results show the effectiveness of the proposed LDA-based method compared to the classical TF-IDF-Gini approach in the context of highly imperfect automatic transcriptions. Finally, we discuss the impact of discriminative and non-discriminative words extracted by both methods in terms of transcription accuracy.
Anthology ID:
L14-1621
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
1309–1314
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/8_Paper.pdf
DOI:
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PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/8_Paper.pdf