We explore the problem of translating speech to text in low-resource scenarios where neither automatic speech recognition (ASR) nor machine translation (MT) are available, but we have training data in the form of audio paired with text translations. We present the first system for this problem applied to a realistic multi-speaker dataset, the CALLHOME Spanish-English speech translation corpus. Our approach uses unsupervised term discovery (UTD) to cluster repeated patterns in the audio, creating a pseudotext, which we pair with translations to create a parallel text and train a simple bag-of-words MT model. We identify the challenges faced by the system, finding that the difficulty of cross-speaker UTD results in low recall, but that our system is still able to correctly translate some content words in test data.
|Title of host publication||Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers|
|Place of Publication||Valencia, Spain|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||6|
|Publication status||Published - 7 Apr 2017|
|Event||15th EACL 2017 Software Demonstrations - Valencia, Spain|
Duration: 3 Apr 2017 → 7 Apr 2017
|Conference||15th EACL 2017 Software Demonstrations|
|Abbreviated title||EACL 2017|
|Period||3/04/17 → 7/04/17|
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- School of Informatics - Reader
- Institute of Language, Cognition and Computation
- Language, Interaction and Robotics
Person: Academic: Research Active