Evaluating Code-Switched Malay-English Speech Using Time Delay Neural Networks.

Abstract

This paper presents a new baseline for Malay-English code-switched speech corpus; which is constructed using a factored form of time delay neural networks (TDNN-F), which reflected a significant relative percentage reduction of 28.07% in the word-error rate (WER), as compared to the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM). The presented results also confirm the effectiveness of time delay neural networks (TDNNs) for code-switched speech.