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Searching for information about a specific person is an online activityfrequently performed by many users. In most cases, users are aided by queriescontaining a name and sending back to the web search engines for finding theirwill. Typically, Web search engines provide just a few accurate resultsassociated with a name-containing query. Currently, most solutions forsuggesting synonyms in online search are based on pattern matching and phoneticencoding, however very often, the performance of such solutions is less thanoptimal. In this paper, we propose SpokenName2Vec, a novel and generic approachwhich addresses the similar name suggestion problem by utilizing automatedspeech generation, and deep learning to produce spoken name embeddings. Thissophisticated and innovative embeddings captures the way people pronounce namesin any language and accent. Utilizing the name pronunciation can be helpful forboth differentiating and detecting names that sound alike, but are writtendifferently. The proposed approach was demonstrated on a large-scale datasetconsisting of 250,000 forenames and evaluated using a machine learningclassifier and 7,399 names with their verified synonyms. The performance of theproposed approach was found to be superior to 10 other algorithms evaluated inthis study, including well used phonetic and string similarity algorithms, andtwo recently proposed algorithms. The results obtained suggest that theproposed approach could serve as a useful and valuable tool for solving thesimilar name suggestion problem.

How Does That Sound? Multi-Language SpokenName2Vec Algorithm Using Speech Generation and Deep Learning