Summary of the paper
| Title | A LDA-based Topic Classification Approach from highly Imperfect Automatic Transcriptions |
|---|---|
| Authors | Mohamed Morchid, Richard Dufour and Georges Linares |
| 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. |
| Topics | Speech Recognition/Understanding, Information Extraction, Information Retrieval |
Full paper  |
A LDA-based Topic Classification Approach from highly Imperfect Automatic Transcriptions |
| Bibtex | @InProceedings{MORCHID14.8, author = {Mohamed Morchid and Richard Dufour and Georges Linares}, title = {A LDA-based Topic Classification Approach from highly Imperfect Automatic Transcriptions}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |