Speech-to-text voice visualization

A computer program product for generating personalized audio data, the computer program product comprising one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions including instructions to:

• receive user input data, wherein the user input data is audio;
• segment the user input data into a set of sentences wherein segmenting the user input data into a set of sentences further includes converting the audio to text;
• generate, for each sentence in the set of sentences, a voice image, wherein the voice image includes at least one pronunciation tag and wave line associated with a sentence and the wave line is a wavy line used to mark a height of intonation of each word of each sentence in the set of sentences, wherein the voice image is generated based on a volume and inflection point of each word in the sentence, and the volume is scaled to each other word in the sentence;
• store the voice image in a temporary table;
• map, based on the wave line and the pronunciation tag, each sentence of the voice image to an emotion;
• modify the user input data based, at least in part on, the wave line and pronunciation tag of the voice image, wherein the modifying includes generating an audio output based on the mapping, playing the output, and tagging each sentence of the input with the mapped emotion;
• and
• train a learning model with the voice image, wherein the training is configured to cause the learning model to generate speech based on the user input data, wherein the audio output includes the mapped emotion in text form.

A computer system for generating personalized audio data, comprising:

• one or more computer processors;
• one or more computer readable storage media;
• and
• computer program instructions, the computer program instructions being stored on the one or more computer readable storage media for execution by the one or more computer processors, the computer program instructions including instructions to:
• receive user input data, wherein the user input data is audio;
• segment the user input data into a set of sentences, wherein segmenting the user input data into a set of sentences further includes converting the audio to text;
• generate, for each sentence in the set of sentences, a voice image, wherein the voice image includes at least one pronunciation tag and wave line associated with a sentence and the wave line is a wavy line used to mark a height of intonation of each word of each sentence in the set of sentences, wherein the voice image is generated based on a volume and inflection point of each word in the sentence, and the volume is scaled to each other word in the sentence;
• store the voice image in a temporary table;
• map, based on the wave line and the pronunciation tag, each sentence of the voice image to an emotion;
• modify the user input data based, at least in part on, the wave line and pronunciation tag of the voice image, wherein the modifying includes generating an audio output based on the mapping, playing the output, and tagging each sentence of the input with the mapped emotion;
• and
• train a learning model with the voice image, wherein the training is configured to cause the learning model to generate speech based on the user input data, wherein the audio output includes the mapped emotion in text form.