Missing data techniques for robust speech recognition

In noisy listening conditions, the information available on which to base speech recognition decisions is necessarily incomplete: some spectro-temporal regions are dominated by other sources. We report on the application of a variety of techniques for missing data in speech recognition. These techniques may be based on marginal distributions or on reconstruction of missing parts of the spectrum. Application of these ideas in the Resource Management task shows performance which is robust to random removal of up to 80% of the frequency channels, but falls off rapidly with deletions which more realistically simulate masked speech. We report on a vowel classification experiment designed to isolate some of the RM problems for more detailed exploration. The results of this experiment confirm the general superiority of marginals-based schemes, demonstrate the viability of shared covariance statistics, and suggest several ways in which performance improvements on the larger task may be obtained.