Articulation and Intelligibility by Jont B. Allen

By Jont B. Allen

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

There appear to be a good number of theories, or issues of view, on how human speech attractiveness features, but few of those theories are finished. what's wanted is a suite of versions which are supported by means of experimental commentary, that symbolize how human speech acceptance relatively works. ultimately there's the sensible challenge of establishing a laptop recognizer. a technique to do that is to construct a desktop recognizer in response to the reversed engineering of human reputation. This has no longer been the normal method of automated speech attractiveness (ASR).

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

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CIRCA 1947–2001 In this section we shall deal with the important issues of entropy and chance, plus some restricted issues regarding context. George A. Miller was the first to explore the use of information theory in both HSR and human language processing (HLP). Miller and his colleagues raised and clarified these issues in some key speech papers. In one classic study Miller was the first to use closed-sets to control the entropy of the listening task. By doing this, it was possible to study the importance of chance as an independent variable.

283) λ(SNR) ≡ v/c . 5) Fletcher went to some trouble to discuss the effect of this ratio on the average phone score s (this key argument is rarely, if ever, acknowledged), and showed that λ has a surprisingly small effect on s . These observations might be important in applications of AI theory to various languages if λ were significantly different from that for English. Another implication is that this insensitivity may reflect the much higher rate of recognition of vowels over consonants at moderate SNRs.

The experiment: Articulation testing consists of playing nonsense syllables com- posed of 60% CVC and 20% each of CV and VC sounds. 1:). This use of balanced nonsense speech sounds approximately maximizes the entropy of the corpus. This was an important method, first used in about 1921, to control for context effects, which were recognized as having a powerful influence on the recognition score Pc . The speech corpus was held constant across experiments to guarantee that the source entropy was constant.

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