Real-time probabilistic segmentation for segment-based speech recognition
Steven C. Lee, James Glass · 1998
In this work, we investigate modifications to a probabilistic segmentation algorithm to achieve a real-time, and pipelined capability for our segment-based speech recognizer [4]. The existing algorithm used a Viterbi and backwards A search to hypothesize phonetic segments [2]. We were able to reduce the computational requirements of this algorithm by reducing the effective search space to acoustic landmarks, and were able to achieve pipelined capability by executing theA search in blocks defined by reliably detected phonetic boundaries. The new algorithm produces 30% fewer segments, and improves TIMIT phonetic recognition performance by 2.4% over an acoustic segmentation baseline. We were also able to produce 30% fewer segments on a word recognition task in a weather information domain [11].