Automatic Optimization of Speech Decoder Parameters

Modern speech decoders are complex with potentially a large number of parameters that allow tuning for performance and speed. In this paper we investigate methods for automatic optimization of such parameters. The objective is to find the optimal configuration that yields minimal search errors for any real-time factor. We propose a solution for this multiobjective optimization problem based on automatic tracking of that optimal curve. Two cost functions for tracking are investigated as well as techniques to enhance stability. Experiments, conducted using the large vocabulary speech decoder HDecode from the Hidden Markov Model toolkit, show on a large test set of conversation telephone speech that with modest computational cost optimal performance curves for specific decoders and data types can be obtained. Careful selection of the cost function allows a further reduction of computational cost by 55%. As no prior knowledge about the interpretation of the parameters is used, the proposed method is applicable to other decoders.