On-line Memory-Based Parametric Equalization to multimodal training conditions

This paper describes the conceptual and algorithmic evolutions of Memory Based Parametric Equalization (MPEQ) needed to exploit the potentialities of the method within the state-of-the-art Loquendo ASR. MPEQ is the memory-based evolution of Parametric Non-Linear Equalization (PEQ) introduced to overcome the problem of unreliable statistics estimation in presence of very limited acoustic information in the test utterance to be normalized. The main limitations of the method that prevented its practical application were the lack of online implementation, the unrealistic unimodal assumption about the training statistics, the unconditioned application of equalization, and the need for retraining the acoustic models. The paper describes how these limitations have been overcome and reports a large experimentation on many corpora that shows improvements in a variety of mismatched conditions, while preserving performances in matched conditions.