10. A computer program product for imbuing an artificial intelligence system with idiomatic traits, the computer program product comprising a tangible computer readable storage medium having program code embodied therewith, wherein the program code is readable and executable by a processor to perform a method comprising: collecting electronic units of speech from an electronic stream of speech, wherein the electronic stream of speech is generated by a first entity; identifying tokens from the electronic stream of speech, wherein each token identifies a particular electronic unit of speech from the electronic stream of speech, and wherein identification of the tokens is semantic-free such that the tokens are identified independently of a semantic meaning of a respective electronic unit of speech; populating nodes in a first speech graph with the tokens; identifying a first shape of the first speech graph; matching the first shape to a second shape, wherein the second shape is of a second speech graph from a second entity in a known category; assigning the first entity to the known category in response to the first shape matching the second shape; modifying synthetic speech generated by an artificial intelligence system based on the first entity being assigned to the known category, wherein said modifying imbues the artificial intelligence system with idiomatic traits of persons in the known category; and incorporating the artificial intelligence system with the idiomatic traits of persons in the known category into a robotic device in order to align the robotic device with cognitive traits of the persons in the known category.
10. A computer program product for imbuing an artificial intelligence system with idiomatic traits, the computer program product comprising a tangible computer readable storage medium having program code embodied therewith, wherein the program code is readable and executable by a processor to perform a method comprising: collecting electronic units of speech from an electronic stream of speech, wherein the electronic stream of speech is generated by a first entity; identifying tokens from the electronic stream of speech, wherein each token identifies a particular electronic unit of speech from the electronic stream of speech, and wherein identification of the tokens is semantic-free such that the tokens are identified independently of a semantic meaning of a respective electronic unit of speech; populating nodes in a first speech graph with the tokens; identifying a first shape of the first speech graph; matching the first shape to a second shape, wherein the second shape is of a second speech graph from a second entity in a known category; assigning the first entity to the known category in response to the first shape matching the second shape; modifying synthetic speech generated by an artificial intelligence system based on the first entity being assigned to the known category, wherein said modifying imbues the artificial intelligence system with idiomatic traits of persons in the known category; and incorporating the artificial intelligence system with the idiomatic traits of persons in the known category into a robotic device in order to align the robotic device with cognitive traits of the persons in the known category. 14. The computer program product of claim 10 , wherein the first entity is a person, wherein the electronic stream of speech is composed of words spoken by the person, and wherein the method further comprises: generating a syntactic vector ({right arrow over (w)} syn ) of the words, wherein the syntactic vector describes a lexical class of each of the words; creating a hybrid graph (G) by combining the first speech graph and a semantic graph of the words spoken by the person, wherein the hybrid graph is created by: converting the words into semantic vectors, wherein a semantic similarity (sim(a,b)) between two words a and b are estimated by a scalar product (·) of their respective semantic vectors ({right arrow over (w)} a ·{right arrow over (w)} b ), such that: 
 sim( a,b )= {right arrow over (w)} a ·{right arrow over (w)} b ; and creating the hybrid graph (G) of the first speech graph and the semantic graph, where: 
 G={N,E,{right arrow over (W)}} wherein N are nodes, in the hybrid graph, that represent words, E represents edges that represent temporal precedence in the electronic stream of speech, and {right arrow over (W)} is a feature vector, for each node in the hybrid graph, and wherein {right arrow over (W)} is defined as a direct sum of the syntactic vector ({right arrow over (w)} syn ) and semantic vectors ({right arrow over (w)} sem ), plus an additional direct sum of non-textual features ({right arrow over (w)} ntxt ) of the person speaking the words, such that: 
 {right arrow over (W)}={right arrow over (w)} syn ⊕{right arrow over (w)} sem ⊕{right arrow over (w)} ntxt ; and further adjusting the synthetic speech based on a shape of the hybrid graph (G).