Non-Linear Pairwise Language Mappings for Low-Resource Multilingual Acoustic Model Fusion

• URL: http://arxiv.org/abs/2207.03391v1
• Date: Thu, 7 Jul 2022 15:56:50 GMT
• Title: Non-Linear Pairwise Language Mappings for Low-Resource Multilingual Acoustic Model Fusion
• Authors: Muhammad Umar Farooq, Darshan Adiga Haniya Narayana, Thomas Hain
• Abstract summary: 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.
• Score: 26.728287476234538
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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