Immediately following the Second World War, between 1947 and 1955, several classic papers quantified the fundamentals of human speech information processing and recognition. In 1947 French and Steinberg published their classic study on the articulation index. In 1948 Claude Shannon published his famous work on the theory of information. In 1950 Fletcher and Galt published their theory of the articulation index, a theory that Fletcher had worked on for 30 years, which integrated his classic works on loudness and speech perception with models of speech intelligibility. In 1951 George Miller then wrote the first book Language and Communication, analyzing human speech communication with Claude Shannon's just published theory of information. Finally in 1955 George Miller published the first extensive analysis of phone decoding, in the form of confusion matrices, as a function of the speech-to-noise ratio. This work extended the Bell Labs' speech articulation studies with ideas from Shannon's Information theory. Both Miller and Fletcher showed that speech, as a code, is incredibly robust to mangling distortions of filtering and noise.
Regrettably much of this early work was forgotten. While the key science of information theory blossomed, other than the work of George Miller, it was rarely applied to aural speech research. The robustness of speech, which is the most amazing thing about the speech code, has rarely been studied.
It is my belief (i.e., assumption) that we can analyze speech intelligibility with the scientific method. The quantitative analysis of speech intelligibility requires both science and art. The scientific component requires an error analysis of spoken communication, which depends critically on the use of statistics, information theory, and psychophysical methods. The artistic component depends on knowing how to restrict the problem in such a way that progress may be made. It is critical to tease out the relevant from the irrelevant and dig for the key issues. This will focus us on the decoding of nonsense phonemes with no visual component, which have been mangled by filtering and noise.
This monograph is a summary and theory of human speech recognition. It builds on and integrates the work of Fletcher, Miller, and Shannon. The long-term goal is to develop a quantitative theory for predicting the recognition of speech sounds. In Chapter 2 the theory is developed for maximum entropy (MaxEnt) speech sounds, also called nonsense speech. In Chapter 3, context is factored in. The book is largely reflective, and quantitative, with a secondary goal of providing an historical context, along with the many deep insights found in these early works.
Table of Contents
Discussion with Historical Context
About the Author(s)Jont B. Allen
, Beckman Institute for Advanced Science and Technology University of Illinois at Champaign-Urbana
Jont B. Allen received his B.S. in electrical engineering from the University of Illinois, Urbana-Champaign, in 1966, and his M.S. and Ph.D. in electrical engineering from the University of Pennsylvania in 1968 and 1970, respectively. After graduation he joined Bell Laboratories, and was in the Acoustics Research Department in Murray Hill, NJ, from 1974-1996, as a distinguished member of technical staff. Since 1996 Dr. Allen has been a Technology Leader at AT&T Labs-Research. Since Aug. 2003, Allen is an Associate Professor in ECE, at the University of Illinois, and on the research staff of the Beckman Inst., Urbana IL. During his 32 year AT&T Bell Labs (and later AT&T Labs) career Dr. Allen has specialized in auditory signal processing. In the last 10 years he has concentrated on the problem of human speech recognition. His expertise spans the areas of signal processing, physical acoustics, cochlear modeling, auditory neurophysiology, auditory psychophysics, and human speech recognition. Dr. Allen is a fellow (May 1981) of the Acoustical Society of America (ASA) and fellow ( January 1985) of the Institute of Electrical and Electronic Engineers (IEEE). In 1986 he was awarded the IEEE Acoustics Speech and Signal Processing (ASSP) Society Meritorious Service Award, and in 2000 received an IEEE Third Millennium Medal.