How Does That Sound? Multi–Language SpokenName2Vec Algorithm Using Speech Generation and Deep Learning
Table 1: Performance Obtained by the SpokenName2Vec and other algorithms
['Method', 'Accuracy', 'F1', 'AP@1', 'AP@2', 'AP@3', 'AP@5', 'AP@10', 'Recall']
For example, the English SpokenName2Vecmethod obtained an average precision@1 score of 0.147, whereas Soundex and edit distance obtained scores of 0.06 and 0.045, respectively. Given this, we can conclude that the suggested name representation based on speech embedding is much more effective and accurate than the plain text code produced by phonetic algorithms. For example, the average F1 score obtained by the English SpokenName2Vecversion was 0.181 as opposed to 0.152 obtained by the FTG (Net + PE + SS). With respect to precision, we can see that the FTGs outperformed spokennametovec with k=1,2,3. However, the picture changes when k increases (k=5,10) as in those cases, SpokenName2Vecoutperformed the FTGs. It is important to understand that the algorithms are totally different from one another; SpokenName2Vecis capable of detecting names that sound alike, but are written differently. In constrast, FTG suggests similar names based on historical ancestral relationships that are not necessarily related to sound. We can therefore conclude from these results that speech, as well as ancestral trees, can be utilized to improve the similar name suggestion. We believe that future research in which these two approaches are combined should be very helpful and effective for this purpose.