AUDIO FILE LABELING PROCESS FOR BUILDING DATASETS AT SCALE

摘要

An audio labeling tool is described for rapidly and efficiently building a labeled data set preferably comprising audio files with annotations denoting speaker transcriptions, speaker identity, periods of silence, background noise, and speaker emotion labels. The tool provides a configurable user interface (UI) with keyboard shortcuts and menu items for streamlined user-guided markup of audio files with context specific labeling and transcription notes. Human audio file labelers will preferably leverage the labeling tool and configurable user interface and menu to rapidly annotate, validate, and build labeled data sets, at scale, of time sliced audio files. The audio labeling tool is preferably applied to audio files and provides an automated means for adding notations, markup, text transcription, and feature labeling to an audio waveform, spectrogram, or other audio data visualization domain for amplifying and highlighting audio feature details. Labeled audio data is preferably used to train computer models, develop pattern recognition algorithms, and neural networks for automated processing, pre-populating and labeling of significantly large audio data sets at scale.