19 research outputs found
Towards Zero-shot Learning for Automatic Phonemic Transcription
Automatic phonemic transcription tools are useful for low-resource language
documentation. However, due to the lack of training sets, only a tiny fraction
of languages have phonemic transcription tools. Fortunately, multilingual
acoustic modeling provides a solution given limited audio training data. A more
challenging problem is to build phonemic transcribers for languages with zero
training data. The difficulty of this task is that phoneme inventories often
differ between the training languages and the target language, making it
infeasible to recognize unseen phonemes. In this work, we address this problem
by adopting the idea of zero-shot learning. Our model is able to recognize
unseen phonemes in the target language without any training data. In our model,
we decompose phonemes into corresponding articulatory attributes such as vowel
and consonant. Instead of predicting phonemes directly, we first predict
distributions over articulatory attributes, and then compute phoneme
distributions with a customized acoustic model. We evaluate our model by
training it using 13 languages and testing it using 7 unseen languages. We find
that it achieves 7.7% better phoneme error rate on average over a standard
multilingual model.Comment: AAAI 202
Universal Phone Recognition with a Multilingual Allophone System
Multilingual models can improve language processing, particularly for low
resource situations, by sharing parameters across languages. Multilingual
acoustic models, however, generally ignore the difference between phonemes
(sounds that can support lexical contrasts in a particular language) and their
corresponding phones (the sounds that are actually spoken, which are language
independent). This can lead to performance degradation when combining a variety
of training languages, as identically annotated phonemes can actually
correspond to several different underlying phonetic realizations. In this work,
we propose a joint model of both language-independent phone and
language-dependent phoneme distributions. In multilingual ASR experiments over
11 languages, we find that this model improves testing performance by 2%
phoneme error rate absolute in low-resource conditions. Additionally, because
we are explicitly modeling language-independent phones, we can build a
(nearly-)universal phone recognizer that, when combined with the PHOIBLE large,
manually curated database of phone inventories, can be customized into 2,000
language dependent recognizers. Experiments on two low-resourced indigenous
languages, Inuktitut and Tusom, show that our recognizer achieves phone
accuracy improvements of more than 17%, moving a step closer to speech
recognition for all languages in the world.Comment: ICASSP 202