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.

本研究使用零样本学习的方法解决语言无训练数据情况下语音转写的挑战，将语音音素分解成相应的发音属性，结合定制的声学模型，实现对目标语言中未知音素的识别，评估结果表明，该方法比传统多语言模型的平均音素误差率提高了 7.7%。