Using untranscribed training data to improve performance
George Zavaliagkos, Man-Hung Siu, Thomas Colthurst, Jayadev Billa · 1998
This paper explores techniques for utilizing untranscribed training data pools to increase the available training data for automatic speech recognition systems. It has been well estab-lished that current speech recognition technology, especially in Large Vocabulary Conversational Speech Recognition (LVCSR), is largely language independent, and that the dominant factor with regards to performance on a certain language is the amount of available training data ([4]). The paper addresses this need for increased training data by presenting ways to use untranscribed acoustic data to increase the training data size and thus improve speech recognition. 1.