Title
Adapting Machine Translation Models Toward Misrecognized Speech With Text-To-Speech Pronunciation Rules And Acoustic Confusability
Abstract
In the spoken language translation pipeline, machine translation systems that are trained solely on written bitexts are often unable to recover from speech recognition errors due to the mismatch in training data. We propose a novel technique to simulate the errors generated by an ASR system, using the ASR system's pronunciation dictionary and language model. Lexical entries in the pronunciation dictionary are converted into phoneme sequences using a text-to-speech (TTS) analyzer and stored in a phoneme-to-word translation model. The translation model and ASR language model are combined into a phoneme to-word MT system that "damages" clean texts to look like ASR outputs based on acoustic confusions. Training texts are TTS-converted and damaged into synthetic ASR data for use as adaptation data for training a speech translation system. Our proposed technique yields consistent improvements in translation quality on English-French lectures.
Year
Venue
Keywords
2015
16TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2015), VOLS 1-5
spoken language translation, machine translation, pronunciation modeling, error modeling
Field
DocType
Citations 
Pronunciation,Training set,Spoken language translation,Speech synthesis,Computer science,Machine translation,Speech recognition,Speech translation,Language model
Conference
1
PageRank 
References 
Authors
0.38
17
4
Name
Order
Citations
PageRank
Nick Ruiz1635.23
Qin Gao220211.76
William D. Lewis317118.53
marcello federico42420179.56