Toward the Ultimate ASR Language Model

Abstract

The n-gram model is standard for large vocabulary speech recognizers. Many attempts were made to improve on it. Language models were proposed based on grammatical analysis, artificial neural networks, random forests, etc. While the latter give somewhat better recognition results than the n-gram model, they are not practical, particularly when large training data bases (e.g., from world wide web) are available. So should language model research be abandoned as a hopeless endeavor? This talk will discuss a plan to determine how large a decrease in recognition error rate is conceivable, and propose a game-based method to determine what parameters the ultimate language model should depend on.