@article {Layton:August 2007:0922-5773:67,
	author = "Layton, Martin",
	author = "Gales, Mark",
	title = "Acoustic Modelling Using Continuous Rational Kernels",
	journal = "The Journal of VLSI Signal Processing",
	volume = "48",
	year = "August 2007",
	abstract = "Many discriminative classification algorithms are designed for tasks where samples can be represented by fixed-length vectors. However, many examples in the fields of text processing, computational biology and speech recognition are best represented as variable-length sequences of vectors. Although several dynamic kernels have been proposed for mapping sequences of discrete observations into fixed-dimensional feature-spaces, few kernels exist for sequences of continuous observations. This paper introduces continuous rational kernels, an extension of standard rational kernels, as a general framework for classifying sequences of continuous observations. In addition to allowing new task-dependent kernels to be defined, continuous rational kernels allow existing continuous dynamic kernels, such as Fisher and generative kernels, to be calculated using standard weighted finite-state transducer algorithms. Preliminary results on both a large vocabulary continuous speech recognition (LVCSR) task and the TIMIT database are presented.",
	pages = "67-82(16)",
	url = "http://www.ingentaconnect.com/content/klu/vlsi/2007/00000048/F0020001/00000027"
	doi = "doi:10.1007/s11265-006-0027-4"
}