ISCA Archive Interspeech 2014
ISCA Archive Interspeech 2014

Factor analysis based semantic variability compensation for automatic conversation representation

Mohamed Bouallegue, Mohamed Morchid, Richard Dufour, Driss Matrouf, Georges Linarès, Renato De Mori

The main objective of this paper is to identify themes from dialogues of telephone conversations in a real-life customer care service. In this task, the word semantic variability contained in these conversations may impact the classification performance by retaining the noise in their vectorial representation. In this article, we propose an original method to compensate this semantic variability using the Factor Analysis (FA) paradigm, initially designed for speech processing tasks to compensate the acoustic variability, mainly in Speaker Verification (SV) and Automatic Speech Recognition (ASR). In our proposal, we used the FA paradigm to estimate the semantic variability as an additive component located in a subspace of low dimension (with respect to the super-vector space). This additive semantic variability is estimated in Factor Analysis model space. From this estimation, a specific vector transformation is obtained and is applied to vectors of dialogue representation. Experiments are reported using a corpus collected in the call center of the Paris Transportation Service. Results show the effectiveness of the proposed representation paradigm with a theme identification accuracy of 80.0%, showing a significant improvement with respect to previous results on the same corpus.


doi: 10.21437/Interspeech.2014-427

Cite as: Bouallegue, M., Morchid, M., Dufour, R., Matrouf, D., Linarès, G., Mori, R.D. (2014) Factor analysis based semantic variability compensation for automatic conversation representation. Proc. Interspeech 2014, 1885-1889, doi: 10.21437/Interspeech.2014-427

@inproceedings{bouallegue14b_interspeech,
  author={Mohamed Bouallegue and Mohamed Morchid and Richard Dufour and Driss Matrouf and Georges Linarès and Renato De Mori},
  title={{Factor analysis based semantic variability compensation for automatic conversation representation}},
  year=2014,
  booktitle={Proc. Interspeech 2014},
  pages={1885--1889},
  doi={10.21437/Interspeech.2014-427},
  issn={2308-457X}
}