Vocabulary restrictions in large vocabulary continuous speech recognition (LVCSR) systems mean that out-of-vocabulary (OOV) words are lost in the output. However, OOV words tend to be information rich terms (often named entities) and their omission from the transcript negatively affects both usability and downstream NLP technologies, such as machine translation or knowledge distillation. We propose a novel approach to OOV recovery that uses a spoken term detection (STD) framework. Given an identified OOV region in the LVCSR output, we recover the uttered OOVs by utilizing contextual information and the vast and constantly updated vocabulary on the Web. Discovered words are integrated into the system output, recovering up to 40% of the OOV terms and resulting in a reduction in system error.
Bibliographic reference. Parada, Carolina / Sethy, Abhinav / Dredze, Mark / Jelinek, Frederick (2010): "A spoken term detection framework for recovering out-of-vocabulary words using the web", In INTERSPEECH-2010, 1269-1272.