When translating from speech, special consideration for conversational speech phenomena such as disfluencies is necessary. Most machine translation training data consists of wellformed written texts, causing issues when translating spontaneous speech. Previous work has introduced an intermediate step between speech recognition (ASR) and machine translation (MT) to remove disfluencies, making the data better-matched to typical translation text and significantly improving performance. However, with the rise of end-to-end speech translation systems, this intermediate step must be incorporated into the sequence-to-sequence architecture. Further, though translated speech datasets exist, they are typically news or rehearsed speech without many disfluencies (e.g. TED), or the disfluencies are translated into the references (e.g. Fisher). To generate clean translations from disfluent speech, cleaned references are necessary for evaluation. We introduce a corpus of cleaned target data for the Fisher Spanish-English dataset for this task. We compare how different architectures handle disfluencies and provide a baseline for removing disfluencies in end-to-end translation. Index Termsspeech translation, disfluency removal, spoken language translation, spoken language processing 2 LDC2010S01 and LDC2010T04 3 joshua-decoder.org/fisher-callhome-corpus 921 978-1-5386-4334-1/18/$31.00
REF introduced a set of fluent references 1 for Fisher Spanish-English, enabling a new task: end-to-end training and evaluation against fluent references.
Towards Fluent Translations From Disfluent Speech
2018 IEEE Spoken Language Technology Workshop (SLT)
2018 IEEE Spoken Language Technology Workshop (SLT)
Computer Science