Articulation and Intelligibility by Jont B. Allen PDF

By Jont B. Allen

ISBN-10: 1598290088

ISBN-13: 9781598290080

This lecture is a evaluation of what's recognized approximately modeling human speech reputation (HSR). A version is proposed, and information are proven opposed to the version.

There appear to be a great number of theories, or issues of view, on how human speech acceptance capabilities, but few of those theories are accomplished. what's wanted is a collection of types which are supported by means of experimental remark, that signify how human speech acceptance fairly works. eventually there's the sensible challenge of creating a laptop recognizer. a technique to do that is to construct a computer recognizer in accordance with the reversed engineering of human popularity. This has now not been the normal method of computerized speech reputation (ASR).

What is required is a few perception into why this huge distinction among human functionality and modern desktop functionality exists. writer Jont Allen addresses this and different questions.

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Extra resources for Articulation and Intelligibility

Example text

2) did a good job of representing MaxEnt CVC syllable recognition, defined by S3 ≡ cvc ≈ s 3 . 3) Similarly, MaxEnt CV and VC phone recognitions were well represented by S2 ≡ (cv + vc )/2 ≈ s 2 . 4) These few simple models worked well over a large range of scores, for both filtering and noise (Rankovic, 2002). Note that these formulae only apply to MaxEnt speech sounds, not meaningful words. 4 Namely such models follow if independence is assumed, but demonstrating their validity experimentally does not prove independence.

Four served as listeners, with a randomly chosen one of the five serving as the live talker (recordings or phonograph records of the speech were not used, as was the case for the Bell studies). The database, was entirely CV speech, with a single vowel, always the /a/ as in the word father. , that phone recognition is grounded on hierarchical categorical discriminations. This conclusion follows from an analysis of the Miller Nicely confusion data. ARTICULATION 49 10 monos (blue) and 10 digits (green) vs.

12)] j increases the score (Pe < Pe , j > 1). Boothroyd (Boothroyd, 1978; Boothroyd and Nittrouer, 1988) addresses the sequential vs parallel processing question in terms of two rules. When the articulations are multiplied, Boothroyd calls it “elements of wholes → wholes,” which requires what he calls a “ j -factor,” as in Pc ≡ p cj . 21) When articulation errors are multiplied, he views the situation as describing a mapping from “no context → context,” which requires what he calls a “k-factor” as in Eq.

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Articulation and Intelligibility by Jont B. Allen

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