Impact of Word Error Rate on theme identification task of highly imperfect human–human conversations. (July 2016) - Morchid, Mohamed; Dufour, Richard; Linarès, Georges
Abstract : Highlights: Review of the impact of dialogue representations and classification methods. We discuss the impact of discriminative words in terms of transcription accuracy. Original study evaluating the impact of the WER in the LDA topic space. Abstract: A review is proposed of the impact of word representations and classification methods in the task of theme identification of telephone conversation services having highly imperfect automatic transcriptions. We firstly compare two word-based representations using the classical Term Frequency-Inverse Document Frequency with Gini purity criteria (TF-IDF-Gini) method and the latent Dirichlet allocation (LDA) approach. We then introduce a classification method that takes advantage of the LDA topic space representation, highlighted as the best word representation. To do so, two assumptions about topic representation led us to choose a Gaussian Process (GP) based method. Its performance is compared with a classical Support Vector Machine (SVM) classification method. Experiments showed that the GP approach is a better solution to deal with the multiple theme complexity of a dialogue, no matter the conditions studied (manual or automatic transcriptions) (Morchid et al., 2014 ). In order to better understand results obtained using different word representation methods and classification approaches, we then discuss the impact of discriminative and non-discriminative words extracted by both word representations methods in terms of transcription accuracy (Morchid et al., 2014 ). Finally, we propose a novel study that evaluates the impact of the Word Error Rate (WER) in the LDA topic space learning process as well as during the theme identification task. This original qualitative study points out that selecting a small subset of words having the lowest WER (instead of using all the words) allows the system to better classify automatic transcriptions with an absolute gain of 0.9 point, in comparison to the best performance achieved on this dialogue classification task (precision of 83.3%).