A computing platform to modify voice data for a man machine interface, the computing platform comprising: an audio input device configured to generate a waveform representation of input voice data, the input voice data comprising a voice command requesting an electronic device to carry out a specific function expressed by the voice command; an audio editor configured to repeatedly modify a parameter of an acoustic phonetic characteristic of the waveform representation to produce a plurality of modified waveform representations having different modified parameters of the acoustic phonetic characteristic, wherein the acoustic phonetic characteristics include at least one of pitch, timbre, volume, power, duration, noise ratios, length of sounds, and filter matches; a trained supervised machine learning, ML, model configured to apply the modified waveform representations to determine probabilities of the modified waveform representations, the modified waveform representations corresponding to different outcomes and an outcome comprising an action of the electronic device, wherein the trained supervised ML model has been trained on input-output pairs, waveform representations of voice data being the input and corresponding probabilities being the output, and wherein the probability of a waveform representation indicates the degree of accurate processing of the waveform representation into action, the probability showing the likeness of the action of the electronic device to an outcome expressed by the voice command; a selection logic configured to select the modified waveform representation associated with the highest probability of the determined probabilities, for which the outcome corresponds to the input voice data; and an output device configured to output the modified waveform representation associated with the highest probability. an audio input device configured to generate a waveform representation of input voice data, the input voice data comprising a voice command requesting an electronic device to carry out a specific function expressed by the voice command; an audio editor configured to repeatedly modify a parameter of an acoustic phonetic characteristic of the waveform representation to produce a plurality of modified waveform representations having different modified parameters of the acoustic phonetic characteristic, wherein the acoustic phonetic characteristics include at least one of pitch, timbre, volume, power, duration, noise ratios, length of sounds, and filter matches; a trained supervised machine learning, ML, model configured to apply the modified waveform representations to determine probabilities of the modified waveform representations, the modified waveform representations corresponding to different outcomes and an outcome comprising an action of the electronic device, wherein the trained supervised ML model has been trained on input-output pairs, waveform representations of voice data being the input and corresponding probabilities being the output, and wherein the probability of a waveform representation indicates the degree of accurate processing of the waveform representation into action, the probability showing the likeness of the action of the electronic device to an outcome expressed by the voice command; a selection logic configured to select the modified waveform representation associated with the highest probability of the determined probabilities, for which the outcome corresponds to the input voice data; and an output device configured to output the modified waveform representation associated with the highest probability.