Multilingual speech recognition has drawn significant attention as an
effective way to compensate data scarcity for low-resource languages.
End-to-end (e2e) modelling is preferred over conventional hybrid systems,
mainly because of no lexicon requirement. However, hybrid DNN-HMMs still
outperform e2e models in limited data scenarios. Furthermore, the problem of
manual lexicon creation has been alleviated by publicly available trained
models of grapheme-to-phoneme (G2P) and text to IPA transliteration for a lot
of languages. In this paper, a novel approach of hybrid DNN-HMM acoustic models
fusion is proposed in a multilingual setup for the low-resource languages.
Posterior distributions from different monolingual acoustic models, against a
target language speech signal, are fused together. A separate regression neural
network is trained for each source-target language pair to transform posteriors
from source acoustic model to the target language. These networks require very
limited data as compared to the ASR training. Posterior fusion yields a
relative gain of 14.65% and 6.5% when compared with multilingual and
monolingual baselines respectively. Cross-lingual model fusion shows that the
comparable results can be achieved without using posteriors from the language
dependent ASR.

在多语种语音识别中，为了弥补低资源语言的数据匮乏，人们广泛使用多功能语音识别。 本文提出了一种新颖的混合 DNN-HMM 声学模型融合方法，实现了多种低资源语言的跨语音识别，且相对于多功能和单语基线，后验融合得到了 14.65％和 6.5％的相对收益。