We consider the discrete-time version of performability modeling of automated manufacturing systems (AMSs) capable of producing multiple part types, when the Markov rewards are random.
Several computer systems and networks involve queueing models with single server queues.
Garbage collection is a fundamental component of the memory management in several software frameworks, therefore it is crucial to model its impact of system performance accurately.
In this paper a discriminant-function-based minimum recognition error rate pattern-recognition approach is described and studied for various applications in speech processing.
This paper demonstrates that linear discriminant analysis using aerodynamic and acoustic features is effective in discriminating speakers with vocal-fold nodules from normal speakers.
The Fabry-Perot interferometer has recently become important as a resonant cavity for electromagnetic radiation at optical frequencies.
Recently, Li et al. proposed a new auditory feature for robust speech recognition in noise environments. The new feature was derived by mimicking closely the function of human auditory process.
In this work, an integrated approach to vector dynamic feature extraction is described in the design of a hidden Markov model (VVD-IHMM) based speech recognizer.
Natural language call classification can be performed using a latent semantic indexing (LSI) matrix, a popular vector-space model used in information retrieval.
In this paper, we show how discriminative training can be used to improve classifiers used in natural language processing, using as an example the task of natural language call routing.
