THE 1999 CMU 10X REAL TIME BROADCAST NEWS TRANSCRIPTION SYSTEM
Mosur Ravishankar, Rita Singh, Bhiksha Raj, Richard M. Stern · 1999
CMU's 10X real time system is the HMM-based SPHINX-III system with a newly developed fast decoder. The fast decoder uses a subvector clustered version of the acoustic models for Gaussian computation and a lexical tree search structure. It was developed in September, 1999, and is currently a first-pass decoder, capable of generating word lattices. It was designed to optimize speed, recognition accuracy as well as memory requirements. For the 1999 Hub 4 evaluation task, the system used two sets of acoustic models- full-bandwidth and narrow-bandwidth. The acoustic models were 6000 senone, 32 Gaussians per state, 3-state HMMs with no skips permitted across states. The system used a single 39 dimensional feature stream consisting of cepstra and cepstral differences. The lattices generated were rescored using a DAG algorithm. The DAG-rescored hypotheses were designated as those of the primary system. The contrastive system consisted of the output of the first pass Viterbi search, with no DAG rescoring of lattices. A trigram language model consisting of 57,000 unigrams, 10 million bigrams and 14.9 million trigrams was used. No adaptation passes were done. In this paper we describe the various components of the primary system. The first-pass word error rate on the 1998 Hub 4 evaluation set was 20.4 % with this system. The overall word error rate scored by NIST for the 1999 Hub 4 evaluation set was 27.6%.