Efficient nearly error-less LVCSR decoding based on incremental forward and backward passes

Abstract

We show that most search errors can be identified by aligning the results of a symmetric forward and backward decoding pass. Based on this knowledge, we introduce an efficient high-level decoding architecture which yields virtually no search errors, and requires virtually no manual tuning. We perform an initial forward- and backward decoding with tight initial beams, then we identify search errors, and then we recursively increment the beam sizes and perform new forward and backward decodings for erroneous intervals until no more search errors are detected. Consequently, each utterance and even each single word is decoded with the smallest beam size required to decode it correctly. On all tested systems we achieve an error rate equal or very close to classical decoding with ideally tuned beam size, but unsupervisedly without specific tuning, and at around 2 times faster runtime. An additional speedup by factor 2 can be achieved by decoding the forward and backward pass in separate threads.