Shelia Guberman (born 25 February 1930, Ukraine, USSR) is a scientist in computer science, nuclear physics, geology, geophysics, medicine, artificial intelligence and perception. He proposed the D-waves theory of Earth seismicity, algorithms of Gestalt-perception (1980) and Image segmentation, and programs for the technology of oil and gas fields exploration (1985).
Life and career He is the son of Aizik Guberman (writer, poet) and his wife Etya (teacher). From 1947 to 1952 Guberman studied at the Institute of Electrical Communications, Odessa, USSR, graduating in radio engineering. From 1952 to 1958 he worked as field geophysicist in the Soviet oil industry. From 1958 to 1961 he studied as a postgraduate at the Oil and Gas Institute in Moscow. In 1962 he received a PhD. in nuclear physics, followed by a PhD. in applied mathematics in 1971. In 1971 he was appointed for full professorship in computer science. After authoring the first applied pattern recognition program in 1962, Guberman specialized in artificial intelligence implementing principles of Gestalt perception in computer programs for geological data analysis. In 1966 he was invited by the outstanding mathematician of the XX century Prof. Israel Gelfand to lead the Artificial Intelligence team in Keldysh Institute of Applied Mathematics of the Russian Academy of Sciences. He applied the pattern recognition technology to earthquake prediction, oil and gas exploration, handwriting recognition, speech compression, and medical imaging. From 1989 to 1992 Guberman held the chair professorship at Moscow Open University (Department of Geography). Since 1992 he is living in the US. Guberman is the inventor of the handwriting recognition technology implemented in the commercial product by the company "Paragraph International" founded by Stepan Pachikov, and used today by Microsoft in Windows CE. He is author of core technologies for five US companies, and owns a patent on speech compression.
Achievements
Handwriting recognition The common approach to computer handwriting recognition was computer learning on a set of examples (characters or words) presented as visual objects. Guberman proposed that it is more adequate for the psycho-physiology of human perception to present the script as a kinematic object, a gesture, i.e. synergy of movements of the stylus producing the script. The handwriting consists of 7 primitives. The variations, which characters undergo during the writing, are restricted by the rule: each element can be transformed only into his neighbor in the ordered sequence of primitives. During the evolution of Latin-like writing acquired resistance to natural variations in character shape: when one of the primitives is substituted by his neighbor the interpretation of the character does not change to another one. Based on this approach two USA companies Paragraph and Parascript developed the first commercial products for on-line and off-line free handwriting recognition, which were licensed by Apple, Microsoft, Boeing, Siemens and others. "Most commercially available natural handwriting software is based on ParaGraph or Parascript technology". The hypothesis that humans perceive the handwriting as well as other linear drawings (in general – the communication signals) not in visual modality but in the motor modality was later confirmed by the discovery of mirror neurons. The difference is that in the classical mirroring phenomena the motor response appears in parallel with the observed movement ("immediate action perception"), and during the handwriting recognition the static stimulus is transformed into a time process by tracing the path of the pen on the paper. In both cases the observer is trying to understand the intention of the correspondent: "the understanding of what the person is doing and why he is doing it, is acquired through a mechanism that directly transforms visual information into a motor format".
Speech parallel coding The speech is traditionally presented as a time sequence of phonemes - vowels and consonants. Each vowel is mainly determined by the relationship between the volume sizes of the front and the back of the voice tract. The ratio is defined by 1) horizontal position of tongue (back–forth), 2) the position of the lips (back-forth), and 3) the size of pharynx that can extend the cavity of the voice tract far back. Most consonants can be described with 3 parameters: 1) place of articulation (lips, teeth and so on), 2) time pattern of interaction with the voice tract (explosive or not), and 3) voiced or not voiced sound. Because of the inertia of the articulatory organs (tongue, lips, jaw) any phoneme interferes with the neighbors and changes its sounding (co-articulation). As a result, each phoneme sounds different in different context. Guberman presents the parallel model of speech production. It states that vowels and consonants are generated not in sequence but in parallel. The two channels manage two different gropes of muscles, which together define the geometry of the voice tract, and, respectively, voice signal. The separation is possible because the generation of vowels and of consonants involves different muscles. For the vowels [o], [u] the lips are managed by muscles Mentalis and Orbicularis Oris for protrusion and rounding, and for [i], [e] by Buccinator and Risorius for retracting the lips. The tongue participate in creating the vowels by innervating Superior Longitudinal and Vertical for lifting and for moving the whole tongue back and forth, and Genioglossus for all consonants articulated in the front of the mouth )when jaw is fixed). For the lip consonant [p], [b], [v], [f] the lips are managed by Labii Inferioris and Orbicularis Oris muscles for moving the lips and the jaw up and down, and Zygomaticus Minor for moving the lower lip back for [v], [f]. From the hypothesis of Parallel Phonetic Coding follows: 1. Because the vowels are defined as a particular ratio of front and back volumes of voice tract, the vowels are present at any moment of the speech (even during silence – the neutral vowel [ə] when no muscle of the voice tract is innervated).
… excerpt ends here. Continue reading the full article.
