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Using advanced phonetics, artificial intelligence (AI) & fuzzy logic in a multi-dimensional process:
SANITIZATION: ensuring the integrity of the information in the databases
NORMALIZATION: formatting & transforming data to a mathematical code (phonetic fingerprint)
CALCULATION: matching the entries between databases by over 44 algorithms accurately and efficiently.
Phonetic fingerprint is a mathematical representation of the pronunciation and phonemes within the name (individual person or other entity). Using automated Real-time technology, supported by computational linguistics, AI-ML layers, using over 48 mathematical algorithms, tracing the phoneme across 40 different languages, transliterations and spelling variations enables accurate name matching.
594360=Archibald=Archimbaud=Arquibaldo=Арчибальд
PHONETICS
– understanding the “sound”
COMPUTATIONAL LINGUISTICS
– convert Sound into Number
DISTANCE ALGORITHMS
– Increase Matching Accuracy
4 Propriety distance algorithms, 2 intra-language & 2 inter-language
ML REGRESSION ANALYSIS
– persistence (using history of past decisions)
ML – FREQUENCY WEIGHING
– frequency of similarities between names
AI FUZZY LOGIC
– ensuring efficiency and effectiveness
AI SUGGESTION ENGINE
– Trend Analysis (combined with
ML-Persistence mechanism)
AI UNDERSANTING NAMES
– Enhancing human Eye & logic
One of the first major problems solved by FinCom.Co’s solution, was introducing systems of notation for speech sounds that would solve known problems of auditory phonetics and descriptive linguistics –
because existing language notations are not universal among dictionaries in languages other than English. Since linguistic theory has its origin in practical problems of descriptive linguistics, the approach of the team is Phonology (and its theoretical developments, such as the phoneme) which deals with the interpretation of sound in language.
