Articulation and Intelligibility

Abstract

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.

Combination of Acoustic Classifiers Based on Dempster-Shafer Theory of Evidence

“…[6]) but this is probably the first attempt to use it for combination of information coming from different acoustic streams. Furthermore, under some assumption, DS combination rule is similar to what is known in the speech recognition community as the Fletcher's "product of errors" (see [7], [8]). …”

Perceptual effects of plosive feature modification

“…The AI measure is a sufficient statistic, composed of the average SNR in critical bands, expressed in dB. In 1921, Fletcher showed that the total average phone error is the product of critical band errors, and showed that P e 1 À P c ¼ e AI min (Allen, 2005b;Fletcher, 1921;French and Steinberg, 1947). Thus, the AI is an objective measure that is proportional to the average log phoneme error (Appendix A of Li et al (2010)).…”

Exploring Monaural Features for Classification-Based Speech Segregation

“…Monaural speech segregation, which is the task of speech segregation from monaural recordings, is important for many real-world applications including robust speech and speaker recognition, audio information retrieval and hearing aids design (see e.g., [1], [7]). However, despite decades of effort, monaural speech segregation still remains one of the hardest problems in signal and speech processing.…”