186 research outputs found
Deciphering Speech: a Zero-Resource Approach to Cross-Lingual Transfer in ASR
We present a method for cross-lingual training an ASR system using absolutely
no transcribed training data from the target language, and with no phonetic
knowledge of the language in question. Our approach uses a novel application of
a decipherment algorithm, which operates given only unpaired speech and text
data from the target language. We apply this decipherment to phone sequences
generated by a universal phone recogniser trained on out-of-language speech
corpora, which we follow with flat-start semi-supervised training to obtain an
acoustic model for the new language. To the best of our knowledge, this is the
first practical approach to zero-resource cross-lingual ASR which does not rely
on any hand-crafted phonetic information. We carry out experiments on read
speech from the GlobalPhone corpus, and show that it is possible to learn a
decipherment model on just 20 minutes of data from the target language. When
used to generate pseudo-labels for semi-supervised training, we obtain WERs
that range from 32.5% to just 1.9% absolute worse than the equivalent fully
supervised models trained on the same data.Comment: Submitted to Interspeech 202
Bilingual Lexicon Induction through Unsupervised Machine Translation
A recent research line has obtained strong results on bilingual lexicon
induction by aligning independently trained word embeddings in two languages
and using the resulting cross-lingual embeddings to induce word translation
pairs through nearest neighbor or related retrieval methods. In this paper, we
propose an alternative approach to this problem that builds on the recent work
on unsupervised machine translation. This way, instead of directly inducing a
bilingual lexicon from cross-lingual embeddings, we use them to build a
phrase-table, combine it with a language model, and use the resulting machine
translation system to generate a synthetic parallel corpus, from which we
extract the bilingual lexicon using statistical word alignment techniques. As
such, our method can work with any word embedding and cross-lingual mapping
technique, and it does not require any additional resource besides the
monolingual corpus used to train the embeddings. When evaluated on the exact
same cross-lingual embeddings, our proposed method obtains an average
improvement of 6 accuracy points over nearest neighbor and 4 points over CSLS
retrieval, establishing a new state-of-the-art in the standard MUSE dataset.Comment: ACL 201
- …