Abstract: A system for an automated real-time transcription and editing of audio data using interim text, including a processor of an audio transcription server (ATS) node configured to host a machine learning (ML) module coupled to at least one audio source entity and connected to at least one user-entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire audio data from the at least one audio source entity; parse out the audio data to derive features for beam forming and features for speaker diarization; generate a set of classifiers based on the features for beam forming and the features for speaker diarization; provide the set of classifiers to the ML module configured to generate a predictive model for producing at least one speaker identification parameter; identify the speaker based on the at least one speaker identification parameter; continuously transcribe the audio data to generate an interim text associated

Abstract: A system for automated real-time transcription and editing of audio data using interim text. The system includes a processor of an audio transcription server (ATS) node configured to host a machine learning (ML) module, coupled to at least one audio source and one user-entity node over a network. The processor executes instructions to: acquire audio data; derive features for beam forming and speaker diarization; generate classifiers and provide them to the ML module; identify speakers based on generated parameters; transcribe the audio into interim text associated with the speaker; derive labels to generate a feature vector; and use the ML module to predict interim text editing parameters. These parameters are provided to the user-entity node to enable real-time editing of the interim text. The system supports enhanced speaker identification and editing workflows, particularly suited for legal transcription, including mono-channel input and secure, profile-based speaker recognition.

Abstract: A system for an automated real-time transcription and editing of audio data using interim text, including a processor of an audio transcription server (ATS) node configured to host a machine learning (ML) module coupled to at least one audio source entity and connected to at least one user-entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire audio data from the at least one audio source entity; parse out the audio data to derive features for beam forming and features for speaker diarization; generate a set of classifiers based on the features for beam forming and the features for speaker diarization; provide the set of classifiers to the ML module configured to generate a predictive model for producing at least one speaker identification parameter; identify the speaker based on the at least one speaker identification parameter; continuously transcribe the audio data to generate an interim text associated