LIA-RAG: a system based on graphs and divergence of probabilities applied to Speech-To-Text Summarization
arXiv:1601.07124v15 citations
AI Analysis
This addresses speech-to-text summarization for noisy conversations, but appears incremental as it builds on existing methods with a specific dataset.
The paper tackles the problem of automatic speech-to-text summarization by introducing a new algorithm based on statistical divergences and graphs, achieving very encouraging results on the CCCS Multiling 2015 French corpus.
This paper aims to introduces a new algorithm for automatic speech-to-text summarization based on statistical divergences of probabilities and graphs. The input is a text from speech conversations with noise, and the output a compact text summary. Our results, on the pilot task CCCS Multiling 2015 French corpus are very encouraging