Learn More
Human speech perception is robust in the face of a wide variety of distortions, both experimentally applied and naturally-occurring. In these conditions, state-of-the-art automatic speech recognition technology fails. This paper describes an approach to robust ASR which acknowledges the fact that some spectro-temporal regions will be dominated by noise. For(More)
  • Martin Cooke
  • 2006
Do listeners process noisy speech by taking advantage of "glimpses"-spectrotemporal regions in which the target signal is least affected by the background? This study used an automatic speech recognition system, adapted for use with partially specified inputs, to identify consonants in noise. Twelve masking conditions were chosen to create a range of(More)
Spoken communication in a non-native language is especially difficult in the presence of noise. This study compared English and Spanish listeners' perceptions of English intervocalic consonants as a function of masker type. Three maskers (stationary noise, multitalker babble, and competing speech) provided varying amounts of energetic and informational(More)
The statistical theory of speech recognition introduced several decades ago has brought about low word error rates for clean speech. However, it has been less successful in noisy conditions. Since extraneous acoustic sources are present in virtually all everyday speech communication conditions, the failure of the speech recognition model to take noise into(More)
Robust speech recognition in everyday conditions requires the solution to a number of challenging problems, not least the ability to handle multiple sound sources. The specific case of speech recognition in the presence of a competing talker has been studied for several decades, resulting in a number of quite distinct algorithmic solutions whose focus(More)
We address the theoretical and practical issues involved in ASR when some of the observation data for the target signal is masked by other signals. Techniques discussed range from simple missing data imputation to Bayesian optimal classification. We have developed the Bayesian approach because this allows prior knowledge to be incorporated naturally into(More)
In this study, techniques for classification with missing or unreliable data are applied to the problem of noise-robustness in Automatic Speech Recognition (ASR). The techniques described make minimal assumptions about any noise background and rely instead on what is known about clean speech. A system is evaluated using the Aurora 2 connected digit(More)
In previous work we h a ve developed the theory and demonstrated the promise of the Missing Data approach to robust Automatic Speech Recognition. This technique is based on hard decisions as to whether each time-frequency \pixel" is either reliable or unreliable. In this paper we replace these discrete decisions with soft estimates of the probability that(More)