Towards task-independent speech recognition

Despite the considerable progress made in the last decade, speech recognition is far from a solved problem. For instance, porting a recognition system to a new task (or language) still requires substantial investment of time and money, as well as expertise in speech recognition. The paper takes a first step at evaluating to what extent a generic state-of-the-art speech recognizer can reduce the manual effort required for system development. We demonstrate the genericity of wide domain models, such as broadcast news acoustic and language models, and techniques to achieve a higher degree of genericity, such as transparent methods to adapt such models to a specific task. This work targets three tasks using commonly available corpora: small vocabulary recognition (TI-digits), text dictation (WSJ), and goal-oriented spoken dialog (ATIS)