NDLI: How do humans process and recognize speech?

Content Provider	IEEE Xplore Digital Library
Author	Allen, J.B.
Copyright Year	1993
Abstract	Until the performance of automatic speech recognition (ASR) hardware surpasses human performance in accuracy and robustness, we stand to gain by understanding the basic principles behind human speech recognition (HSR). This problem was studied exhaustively at Bell Labs between the years of 1918 and 1950 by Harvey Fletcher and his colleagues. The motivation for these studies was to quantify the quality of speech sounds in the telephone plant to both improve speech intelligibility and preference. To do this he and his group studied the effects of filtering and noise on speech recognition accuracy for nonsense consonant-vowel-consonant (CVC) syllables, words, and sentences. Fletcher used the term "articulation" as the probability of correct recognition for nonsense sounds, and "intelligibility" as the probability of correction recognition for words (sounds having meaning). In 1919, Fletcher found a way to transform articulation data for filtered speech into an additive density function D(f) and found a formula that accurately predicts the average articulation. The area under D(S) is called the "articulation index." Fletcher then went on to find relationships between the recognition errors for the nonsense speech sounds, words, and sentences. This work has recently been reviewed and partially replicated by Boothroyd and by Bronkhorst, et al. (1980). Taken as a whole, these studies tell us a great deal about how humans process and recognize speech sounds.<>
Starting Page	567
Ending Page	577
Page Count	11
File Size	1014543
File Format	PDF
ISSN	10636676
Volume Number	2
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-10-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Speech recognition Speech processing Automatic speech recognition Hardware Robustness Performance gain Telephony Filtering Acoustic noise
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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