Novel CI-backoff scheme for real-time embedded speech recognition

A new method for reduction of computation and memory bandwidth for embedded large vocabulary continuous speech recognition is presented. During the Hidden Markov model state likelihood computation, scores for selected context-dependent (triphone) model states are computed for several frames in advance. Scores that are subsequently needed for Viterbi search but not found in the buffer are replaced by the scores for associated context independent (monophone) models. On the Wall Street Journal 20,000 word continuous speech recognition task, an overall reduction of 58% memory bandwidth and decrease of 23% execution time is achieved relative to an assembly optimized implementation of Sphinx 3. Recognition accuracy is reduced by <;1% while recognition latency is increased by 30 milliseconds.