Dealing with Limited and Noisy Data in ASR: a Hybrid Knowledge-based and Statistical Approach

In this talk, Twill focus on the importance of integrating knowledge of human speech production and speech perception mechanisms, and language-specific information with statistically-based, data-driven approaches to develop robust and scalable automatic speech recognition (ASR) systems. As we will demonstrate, the need for such hybrid systems is especially critical when the ASR system is dealing with noisy data, when adaptation data are limited (for the case of speaker normalization and adaptation), and when dealing with accents.