A processor-implemented method, comprising: obtaining at least one speech utterance of a user as input, via one or more hardware processors; and processing the obtained at least one speech utterance, via the one or more hardware processors, comprising: comparing the obtained at least one speech utterance with at least one deviation model; determining a deviation level of the at least one speech utterance, based on the comparison with the at least one deviation model, wherein the deviation level comprises: (a) at least one type of deviation present in the at least one speech utterance; (b) extent of deviation of the at least one speech utterance from a healthy speech, based on the at least one deviation model, wherein the at least one deviation model comprising feature vectors representing degree of deviation of speech signal at different severity levels of at least one speech disorder; and (c) locations of all the deviations in the at least one speech utterance; determining at least one speech modification matching the determined deviation level of the at least one speech utterance; and modifying the obtained at least one speech utterance in all the locations of the deviations, using the determined at least one speech modification. obtaining at least one speech utterance of a user as input, via one or more hardware processors; and processing the obtained at least one speech utterance, via the one or more hardware processors, comprising: comparing the obtained at least one speech utterance with at least one deviation model; determining a deviation level of the at least one speech utterance, based on the comparison with the at least one deviation model, wherein the deviation level comprises: (a) at least one type of deviation present in the at least one speech utterance; (b) extent of deviation of the at least one speech utterance from a healthy speech, based on the at least one deviation model, wherein the at least one deviation model comprising feature vectors representing degree of deviation of speech signal at different severity levels of at least one speech disorder; and (c) locations of all the deviations in the at least one speech utterance; determining at least one speech modification matching the determined deviation level of the at least one speech utterance; and modifying the obtained at least one speech utterance in all the locations of the deviations, using the determined at least one speech modification. comparing the obtained at least one speech utterance with at least one deviation model; determining a deviation level of the at least one speech utterance, based on the comparison with the at least one deviation model, wherein the deviation level comprises: (a) at least one type of deviation present in the at least one speech utterance; (b) extent of deviation of the at least one speech utterance from a healthy speech, based on the at least one deviation model, wherein the at least one deviation model comprising feature vectors representing degree of deviation of speech signal at different severity levels of at least one speech disorder; and (c) locations of all the deviations in the at least one speech utterance; (a) at least one type of deviation present in the at least one speech utterance; (b) extent of deviation of the at least one speech utterance from a healthy speech, based on the at least one deviation model, wherein the at least one deviation model comprising feature vectors representing degree of deviation of speech signal at different severity levels of at least one speech disorder; and (c) locations of all the deviations in the at least one speech utterance; determining at least one speech modification matching the determined deviation level of the at least one speech utterance; and modifying the obtained at least one speech utterance in all the locations of the deviations, using the determined at least one speech modification. <EOS>