Acoustic modeling based on early-to-late reverberation ratio for robust ASR

The effect of reverberation on speech recognition performance has been investigated in several works; the goal of this paper is to illustrate a novel approach for acoustic model selection based on the information carried by the Early-to-Late Reverberation Ratio, assuming that the major impact of reverberation is related to the distribution of speech energy over time. According to a large number of simulated and real impulse responses, it is shown how to effectively select suitable acoustic models for reverberant speech, exploiting a partial knowledge of the actual target environment. Given a set of pre-trained models, a GMM-based scheme is used to select the best model for a given (unknown) reverberant condition. A well-known recognition task of connected digits represents a comprehensive experimental setup that validates the proposed strategy.