Gated Module Neural Network for Multilingual Speech Recognition
Yuan‐Fu Liao, Matúš Pleva, Daniel Hládek, Ján Staš, Peter Viszlay, Martin Lojka, Jozef Juhár · 2018
For most multilingual large vocabulary continuous speech recognition (LVCSR) systems, when multiple languages are allowed at the same time, their performance will degrade significantly due to the strong inter-language competition in the decoding phase. To increase the inter-language discrimination capacity, this paper presents a gated module neural network (GMN) approach that adapts a language identification (LID) component to directly assist the final multilingual LVCSR goal. Thanks to an international collaboration 3 large-scale speech corpora (Mandarin, English and Slovak, denoted as Zh, En and Sk) were shared for studying this problem. Hence the proposed approach was evaluated on both bilingual (Zh/En and Sk/En) and trilingual (Zh/En/Sk) LVCSR tasks. The experimental results show that the proposed GMN is promising and the performance of multilingual LVCSRs are now more comparable with the monolingual ones.