A Trainable Method for the Phonetic Similarity Search in German Proper Names

Abstract

Efficient methods for the similarity search in word databases play a significant role in various applications such as the robust search or indexing of names and addresses, spell-checking algorithms or the monitoring of trademark rights. The underlying distance measures are associated with similarity criteria of the users, and phonetic-based search algorithms are well-established since decades. Nonetheless, rule-based phonetic algorithms exhibit some weak points, e.g. their strong language dependency, the search overhead by tolerance or the risk of missing valid matches vice versa, which causes a pseudo-phonetic functionality in some cases. In contrast, we suggest a novel, adaptive method for similarity search in words, which is based on a trainable grapheme-to-phoneme (G2P) converter that generates most likely and widely correct pronunciations. Only as a second step, the similarity search in the phonemic reference data is performed by involving a conventional string metric such as the Levenshtein distance (LD). The G2P algorithm achieves a string accuracy of up to 99.5% in a German pronunciation lexicon and can be trained for different languages or specific domains such as proper names. The similarity tolerance can be easily adjusted by parameters like the admissible number or likability of pronunciation variants as well as by the phonemic or graphemic LD. As a proof of concept, we compare the G2P-based search method on a German surname database and a telephone book including first name, surname and street name to similarity matches by the conventional Cologne phonetic (Kolner Phonetik, KP) algorithm.