BYBLOS Speech Recognition Benchmark Results

This paper presents speech recognition test results from the BBN BYBLOS system on the Feb 91 DARPA benchmarks in both the Resource Management (RM) and the Air Travel Information System (ATIS) domains. In the RM test, we report on speaker-independent (SI) recognition performance for the standard training condition using 109 speakers and for our recently proposed SI model made from only 12 training speakers. Surprisingly, the 12-speaker model performs as well as the one made from 109 speakers. Also within the RM domain, we demonstrate that state-of-the-art SI models perform poorly for speakers with strong dialects. But we show that this degradation can be overcome by using speaker adaptation from multiple-reference speakers. For the ATIS benchmarks, we ran a new system configuration which first produced a rank-ordered list of the N-best word-sequence hypotheses. The list of hypotheses was then reordered using more detailed acoustic and language models. In the ATIS benchmarks, we report SI recognition results on two conditions. The first is a baseline condition using only training data available from NIST on CD-ROM and a word-based statistical bi-gram grammar developed at MIT/Lincoln. In the second condition, we added training data from speakers collected at BBN and used a 4-gram class grammar. These changes reduced the word error rate by 25%.