  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 and sending back to the web search engines for finding their
will. Typically, Web search engines provide just a few accurate results
associated with a name-containing query. Currently, most 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 approach
which addresses the similar name suggestion problem by utilizing automated
speech generation, and deep learning to produce spoken name embeddings. This
sophisticated and innovative embeddings captures the way people pronounce names
in any language and accent. Utilizing the name 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 and string similarity algorithms, and
two recently proposed algorithms. The results obtained suggest that the
proposed approach could serve as a useful and valuable tool for solving the
similar name suggestion problem.
