How Does it Sound?

Searching for information about a specific person is an online activity frequently performed by many users. In most cases, users are aided by queries containing a name in Web search engines for finding their will. Typically, Web search engines provide just a few accurate results associated with a name-containing query. Most existing solutions for suggesting synonyms in online search are based on pattern matching and phonetic encoding, however very often, the performance of such solutions is less than optimal. In this paper, we propose SpokenName2Vec, a novel and generic algorithm which addresses the similar name suggestion problem by utilizing automated speech generation, and deep learning to produce spoken name embeddings. These sophisticated and innovative embeddings capture the way people pronounce names in any language and accent. Utilizing a name's pronunciation can be helpful for both differentiating and detecting names that sound alike, but are written differently. The proposed approach was demonstrated on a large-scale dataset consisting of 250,000 forenames and evaluated using a machine learning classifier and 7,399 names with their verified synonyms.The performance of the proposed approach was found to be superior to 10 other algorithms evaluated in this study, including well used phonetic encoding and string similarity algorithms, and two recently proposed algorithms (e.g., Name2Vec and GRAFT). The results obtained suggest that the proposed algorithm could serve as a useful and valuable tool for solving the problem of synonym suggestion.