Transcription involves converting spoken audio content into written text. During transcription, annotators listen to the audio and accurately transcribe the spoken words, ensuring the text reflects the content of the audio file. This annotated text can be used for tasks such as closed captioning, subtitling, and content indexing.

Speech annotation for Natural Language Processing (NLP) involves annotating audio data to identify and label speech segments, speaker identities, and linguistic features. Annotators may mark speech boundaries, speaker turns, and speech characteristics such as emotion or sentiment. This annotated data is valuable for training speech recognition systems, sentiment analysis models, and speaker identification algorithms.

Audio classification involves annotating audio files with labels or tags to categorize them into different classes or categories based on their content. Annotators listen to the audio and assign appropriate labels, such as music genre, environmental sounds, or spoken language. This annotated data is used to train machine learning models for tasks such as audio content recommendation, sound event detection, and voice activity detection.