Reshaping automatic speech transcripts for robust high-level spoken document analysis

High-level spoken document analysis is required in many applications seeking access to the semantic content of audio data, such as information retrieval, machine translation or automatic summarization. It is nevertheless a difficult task that is generally based on transcripts provided by an automatic speech recognition system. Unlike standard texts, transcripts belong to the category of highly noisy data because of word recognition errors that affect, in particular, very significant words such as named entities (e.g. person's names, locations, organizations). Transcripts also contain specificities of spoken language that make ineffective their processing by natural language processing tools designed for texts. To overcome these issues, this paper proposes a method to reshape automatic speech transcripts for robust high-level spoken document analysis. The method consists in conceiving a new word-level confidence measure that may efficiently ensure the reliability of transcribed wo...

• Type: Journal
• Year: 2010
• Where: AND
• Authors: Julien Fayolle, Fabienne Moreau, Christian Raymond, Guillaume Gravier
• Keywords: AND 2010, Automatic Speech, Automatic Speech Recognition, Document Analysis, Machine Learning