1. A computer system, comprising: at least one adaptive self-trained computer engine; at least one multi-core processor comprising a plurality of cores, wherein the at least one multi-core processor is associated with the at least one adaptive self-trained computer engine so that the at least one multi-core processor is configured to receive executing instructions from the at least one adaptive self-trained computer engine; wherein the computer system is configured to perform at least the following operations: during a training stage: electronically receiving, by the adaptive self-trained computer engine, an initial speech audio data generated by a microphone of a computing device, wherein the initial speech audio data corresponds to at least one initial utterance received by the microphone from a particular user wherein the at least one initial utterance corresponds to an initial text being read by the particular user; dynamically segmenting, by the adaptive self-trained computer engine, the initial speech audio data and the corresponding initial text into a plurality of user phonemes; dynamically querying, by the adaptive self-trained computer engine, at least one database object containing at least one computer dictionary of stored subject-specific phonetic pronunciations of subject-specific words, which are related to a particular activity, to match the plurality of user phonemes to a plurality of subject-specific phonetic pronunciations to form a plurality of user-specific subject-specific phonemes; dynamically associating, by the adaptive self-trained computer engine, a plurality of first timestamps with the plurality of user-specific subject-specific phonemes, wherein each first timestamp corresponds to a time segment location of a respective matched user phoneme in the initial speech audio data so as to form at least one user-specific subject-specific data dictionary of timestamped user-specific subject-specific phonemes; dynamically generating, by the adaptive self-trained computer engine, a plurality of user-specific subject-specific training instructions by utilizing the at least one user-specific subject-specific data dictionary of timestamped user-specific subject-specific phonemes as a user-specific subject-specific speech training model, wherein each user-specific subject-specific training instruction comprises a training text and a synthesized user-specific speech audio of the training text; dynamically outputting, by the adaptive self-trained computer engine, the training text of each user-specific subject-specific training instruction to the computing device; electronically receiving, by the adaptive self-trained computer engine, a training speech audio data generated by the microphone of the computing device, wherein the training speech audio data corresponds to a training utterance received by the microphone from the particular user wherein the training utterance corresponds to the training text of each user-specific subject-specific training instruction being read by the particular user; dynamically comparing, by the adaptive self-trained computer engine, the training speech audio data to the synthesized user-specific speech audio of the training text to train the user-specific subject-specific speech training model; during a transcription stage: electronically receiving, by the adaptive self-trained computer engine, to-be-transcribed speech audio data of at least one user, wherein the to-be-transcribed speech audio data corresponds to at least one to-be-transcribed utterance received from the at least one user; dynamically splitting, by the adaptive self-trained computer engine, the to-be transcribed speech audio data into a plurality of to-be-transcribed speech audio segments, wherein the splitting is at points of silence to form, after each split, two to-be-transcribed speech audio segments with an identical non-silent audio portion; dynamically associating, by the adaptive self-trained computer engine, a plurality of second timestamps with the plurality of to-be-transcribed speech audio segments, wherein each second timestamp corresponds to a particular time segment location of a respective to-be-transcribed speech audio segment in the to-be-transcribed speech audio data so as to form a plurality or timestamped to-be-transcribed speech audio segments; dynamically assigning, by the adaptive self-trained computer engine, at least one respective timestamped to-be-transcribed speech audio segment of the plurality of timestamped to-be-transcribed speech audio segments to a particular core of the plurality of cores of the at least one multi-core processor; dynamically streaming, by the adaptive self-trained computer engine, the at least one respective timestamped to-be-transcribed speech audio segment of the plurality of timestamped to-be-transcribed speech audio segments to the particular core of the plurality of cores of the at least one multi-core processor at a particular sampling rate for a duration that is n times less than a duration of the at least one to-be-transcribed utterance; dynamically transcribing, in parallel, by the at least one multi-core processor, the plurality of timestamped to-be-transcribed speech audio segments based, at least in part, on the user-specific subject-specific speech training model of the at least one user to generate a plurality of text transcript segments corresponding to the plurality of timestamped to-be-transcribed speech audio segments; wherein the at least one multi-core processor is configured to dynamically transcribe the plurality of timestamped to-be-transcribed speech audio segments such that each timestamped to-be-transcribed speech audio segment is transcribed by at least one core of the plurality of cores of the at least one multi-core processor; dynamically assembling, by the adaptive self-trained computer engine, the plurality of text transcript segments into a user-specific subject-specific output transcript of the to-be-transcribed speech audio data, based, at least in part, on the plurality or second timestamps; dynamically verifying, by the adaptive self-trained computer engine, an accuracy of the user-specific subject-specific output transcript of the to-be-transcribed speech audio data to form a verified user-specific subject-specific output transcript of the to-be-transcribed speech audio data; and dynamically and simultaneously applying, by the adaptive self-trained computer engine, a plurality of filters to the verified user-specific subject-specific output transcript to determine at least one subject-specific message of the at least one user in the to-be-transcribed speech audio data, wherein each filter is a data structure characterizing at least one subject-specific property of at least one particular subject-specific term and at least one context parameter identifying at least one context related to the particular activity, wherein the at least one particular subject-specific term is associated with the at least one context.
1. A computer system, comprising: at least one adaptive self-trained computer engine; at least one multi-core processor comprising a plurality of cores, wherein the at least one multi-core processor is associated with the at least one adaptive self-trained computer engine so that the at least one multi-core processor is configured to receive executing instructions from the at least one adaptive self-trained computer engine; wherein the computer system is configured to perform at least the following operations: during a training stage: electronically receiving, by the adaptive self-trained computer engine, an initial speech audio data generated by a microphone of a computing device, wherein the initial speech audio data corresponds to at least one initial utterance received by the microphone from a particular user wherein the at least one initial utterance corresponds to an initial text being read by the particular user; dynamically segmenting, by the adaptive self-trained computer engine, the initial speech audio data and the corresponding initial text into a plurality of user phonemes; dynamically querying, by the adaptive self-trained computer engine, at least one database object containing at least one computer dictionary of stored subject-specific phonetic pronunciations of subject-specific words, which are related to a particular activity, to match the plurality of user phonemes to a plurality of subject-specific phonetic pronunciations to form a plurality of user-specific subject-specific phonemes; dynamically associating, by the adaptive self-trained computer engine, a plurality of first timestamps with the plurality of user-specific subject-specific phonemes, wherein each first timestamp corresponds to a time segment location of a respective matched user phoneme in the initial speech audio data so as to form at least one user-specific subject-specific data dictionary of timestamped user-specific subject-specific phonemes; dynamically generating, by the adaptive self-trained computer engine, a plurality of user-specific subject-specific training instructions by utilizing the at least one user-specific subject-specific data dictionary of timestamped user-specific subject-specific phonemes as a user-specific subject-specific speech training model, wherein each user-specific subject-specific training instruction comprises a training text and a synthesized user-specific speech audio of the training text; dynamically outputting, by the adaptive self-trained computer engine, the training text of each user-specific subject-specific training instruction to the computing device; electronically receiving, by the adaptive self-trained computer engine, a training speech audio data generated by the microphone of the computing device, wherein the training speech audio data corresponds to a training utterance received by the microphone from the particular user wherein the training utterance corresponds to the training text of each user-specific subject-specific training instruction being read by the particular user; dynamically comparing, by the adaptive self-trained computer engine, the training speech audio data to the synthesized user-specific speech audio of the training text to train the user-specific subject-specific speech training model; during a transcription stage: electronically receiving, by the adaptive self-trained computer engine, to-be-transcribed speech audio data of at least one user, wherein the to-be-transcribed speech audio data corresponds to at least one to-be-transcribed utterance received from the at least one user; dynamically splitting, by the adaptive self-trained computer engine, the to-be transcribed speech audio data into a plurality of to-be-transcribed speech audio segments, wherein the splitting is at points of silence to form, after each split, two to-be-transcribed speech audio segments with an identical non-silent audio portion; dynamically associating, by the adaptive self-trained computer engine, a plurality of second timestamps with the plurality of to-be-transcribed speech audio segments, wherein each second timestamp corresponds to a particular time segment location of a respective to-be-transcribed speech audio segment in the to-be-transcribed speech audio data so as to form a plurality or timestamped to-be-transcribed speech audio segments; dynamically assigning, by the adaptive self-trained computer engine, at least one respective timestamped to-be-transcribed speech audio segment of the plurality of timestamped to-be-transcribed speech audio segments to a particular core of the plurality of cores of the at least one multi-core processor; dynamically streaming, by the adaptive self-trained computer engine, the at least one respective timestamped to-be-transcribed speech audio segment of the plurality of timestamped to-be-transcribed speech audio segments to the particular core of the plurality of cores of the at least one multi-core processor at a particular sampling rate for a duration that is n times less than a duration of the at least one to-be-transcribed utterance; dynamically transcribing, in parallel, by the at least one multi-core processor, the plurality of timestamped to-be-transcribed speech audio segments based, at least in part, on the user-specific subject-specific speech training model of the at least one user to generate a plurality of text transcript segments corresponding to the plurality of timestamped to-be-transcribed speech audio segments; wherein the at least one multi-core processor is configured to dynamically transcribe the plurality of timestamped to-be-transcribed speech audio segments such that each timestamped to-be-transcribed speech audio segment is transcribed by at least one core of the plurality of cores of the at least one multi-core processor; dynamically assembling, by the adaptive self-trained computer engine, the plurality of text transcript segments into a user-specific subject-specific output transcript of the to-be-transcribed speech audio data, based, at least in part, on the plurality or second timestamps; dynamically verifying, by the adaptive self-trained computer engine, an accuracy of the user-specific subject-specific output transcript of the to-be-transcribed speech audio data to form a verified user-specific subject-specific output transcript of the to-be-transcribed speech audio data; and dynamically and simultaneously applying, by the adaptive self-trained computer engine, a plurality of filters to the verified user-specific subject-specific output transcript to determine at least one subject-specific message of the at least one user in the to-be-transcribed speech audio data, wherein each filter is a data structure characterizing at least one subject-specific property of at least one particular subject-specific term and at least one context parameter identifying at least one context related to the particular activity, wherein the at least one particular subject-specific term is associated with the at least one context. 2. The computer system of claim 1 , wherein the computer system is further configured to perform at least the following operations: dynamically analyzing, by the adaptive self-trained computer engine, a frequency spectrum of the initial speech audio data, the training speech audio data, or both, to generate a speaker profile of the particular user.