Language Modeling For Computer-Aided Transcription
Cagdas Kayra Akman, Murat Saraçlar · 2007
Speech recognition and language processing systems require large amounts of transcribed speech corpora. Manual transcription is expensive and slow. Computers may do the same task faster but with more errors. Computer aided transcription is a compromise between these two methods. The output lattices of an ASR engine are manipulated to be used as language models in combination with a letter-based N-gram language model. The combined model is used as the language model of the open source Dasher application. The resulting application allows easy transcription of speech data thanks to the combination of both models at letter level. It is shown that the combined model performs better than both a letter-based N-gram model and models combined at sentence level.