5. A processor implemented method of generating a speech recognition engine, the method comprising: comparing phonemes comprised in training phoneme sequences for words; calculating a substitution probability between each phoneme of the phonemes and all other phonemes of the phonemes; determining phonetic similarities between the training phoneme sequences based on the substitution probability calculated for each phoneme of the training phoneme sequences; calculating phonetic distances between the words based on the determined phonetic similarities between the training phoneme sequences; generating embedding vectors by implementing a multidimensional scaling algorithm to convert the calculated phonetic distances between the words to the embedding vectors arranged on an N-dimensional embedding space; and generating the recognition engine by training, using the generated embedding vectors as training outputs and the training phoneme sequences as training inputs, a recognition model to identify an embedding vector representative of a phoneme sequence that is closest in a phonetic distance to an input phoneme sequence among other positions in the N-dimensional embedding space representative of other phoneme sequences, wherein the training of the recognition model includes repeatedly applying an input training phoneme sequence by the recognition model to identify respective resulting embedding vectors until the recognition model is trained to generate the identified embedding vector representative of a word recognition result for the input phoneme sequence.
6. The method of claim 5 , wherein the calculating of phonetic distances comprises assigning values to the phonetic distances such that, when a phonetic similarity between the training phoneme sequences is large, a phonetic distance between words corresponding to the training phoneme sequences is small.