Acoustic model merging using acoustic models from multilingual speakers for automatic speech recognition

Many studies have explored on the usage of existing multilingual speech corpora to build an acoustic model for a target language. These works on multilingual acoustic modeling often use multilingual acoustic models to create an initial model. This initial model created is often suboptimal in decoding speech of the target language. Some speech of the target language is then used to adapt and improve the initial model. In this paper however, we investigate multilingual acoustic modeling in enhancing an acoustic model of the target language for automatic speech recognition system. The proposed approach employs context dependent acoustic model merging of a source language to adapt acoustic model of a target language. The source and target language speech are spoken by speakers from the same country. Our experiments on Malay and English automatic speech recognition shows relative improvement in WER from 2% to about 10% when multilingual acoustic model was employed.