Multi-stream temporally varying weight regression for cross-lingual speech recognition

Abstract

Building a good Automatic Speech Recognition (ASR) system with limited resources is a very challenging task due to the existing many speech variations. Multilingual and cross-lingual speech recognition techniques are commonly used for this task. This paper investigates the recently proposed Temporally Varying Weight Regression (TVWR) method for cross-lingual speech recognition. TVWR uses posterior features to implicitly model the long-term temporal structures in acoustic patterns. By leveraging on the well-trained foreign recognizers, high quality monophone/state posteriors can be easily incorporated into TVWR to boost the ASR performance on low-resource languages. Furthermore, multi-stream TVWR is proposed, where multiple sets of posterior features are used to incorporate richer (temporal and spatial) context information. Finally, a separate state-tying for the TVWR regression parameters is used to better utilize the more reliable posterior features. Experimental results are evaluated for English and Malay speech recognition with limited resources. By using the Czech, Hungarian and Russian posterior features, TVWR was found to consistently outperform the tandem systems trained on the same features.