Text and Audio Data Labelling
Audio
Prompt + Response Writing (SFT)Text labeling adds context to written language so AI can understand meaning, sentiment, and structure. Key Types Named Entity Recognition (NER): Tagging specific words (e.g., labeling "Apple" as ORGANIZATION or "London" as LOCATION). Sentiment Analysis: Categorizing the emotional tone of text (e.g., marking a product review as POSITIVE, NEGATIVE, or NEUTRAL). Assigning categories to whole documents (e.g., sorting emails into SPAM or INBOX, or news into SPORTS or FINANCE). Intent Classification: Identifying what a user wants from a chatbot phrase (e.g., labeling "Where is my order?" as TRACK_SHIPMENT). A labeler reviews customer support tickets and highlights keywords while assigning a priority level to train automated helpdesk routing tools. 🔊 Audio Data Labeling Audio labeling converts sound waves into structured data that AI models can use to listen and interpret. Audio Transcription: Acoustic logging that converts spoken words into precise text scripts, often including timestamps. Speaker Diarization: Partitioning an audio stream into segments based on who is speaking (e.g., labeling segments as SPEAKER_1 or SPEAKER_2). Acoustic Event Detection: Identifying and tagging specific background sounds (e.g., isolating a CAR_HORN, GLASS_SMASH, or BABY_CRY). Language/Dialect Identification: Labeling the specific language, accent, or regional dialect being spoken in an audio file. Common Work Example A labeler listens to 10-second voice clips from smart assistants, writes out exactly what was said, and tags the speaker's emotional tone as CA E-Commerce Chatbot Intent & NER Training Objective: Train an AI customer service bot to understand user requests and extract order details automatically. Data Used: 5,000 raw customer chat logs and emails. Applied Intent Classification tags to sentences. Performed Named Entity Recognition (NER) to extract data variables. Reduced chatbot routing errors by 25% and automated the retrieval of order statuses.