Improved lattice rescoring by using speech attributes in Large Vocabulary Continuous Speech Recognition systems

Acoustic modeling of Large Vocabulary Continuous Speech Recognition (LVCSR) system which is normally based on context-dependent phone is heavily limited by representative capability between transcriptions and corresponding variation of raw speech utterance. To describe this relationship more accurate, this paper presents an alternative strategy by which speech attributes are used to capture acoustic characteristics to improve performances of LVCSR. Validations on a series of relevant experiments, and it is proven that the speech attributes can be used as complementary knowledge resources that can bring more abundant information than basic phone based system. Hence, speech attribute information is used to be integrated into phone based LVCSR system during lattice rescoring. For both reading and Conversional Telephone Speech (CTS) style LVCSR tasks, experimental results showed that the combined system reduced Word Error Rate (WER) by about 3-5% relatively.