Audio Annotation Services
For Advanced AI Audio Analytics
Audio annotation involves labeling and tagging sound-based data, making it structured, searchable, and machine-readable. It is essential for AI applications such as speech recognition, sound classification, emotion detection, and multilingual translation systems.
Our expert annotators specialize in labeling spoken language, identifying speakers, transcribing conversations, and detecting background sounds to enhance AI-driven audio analytics. Using state-of-the-art audio annotation tools, we ensure high accuracy and reliability for AI-powered speech and sound recognition.
Need to annotate audio files USA-based for AI model training? Akademos provides tailored audio annotation solutions to meet your business needs.
Power Your Audio Data
Turn speech, sounds, and emotions into structured insights with our expert audio annotation
Types of Audio Annotation
We Offer
Label speech, sound, and silence with human-level accuracy, fast, multilingual, and ready to power your voice-enabled AI.
Speech
Transcription
Converts spoken words into digital text, enabling AI to process, interpret, and generate human-like responses.
Applications: Virtual assistants, transcription services, AI-powered customer support.
Speaker
Diarization
Identifies and labels different speakers within an audio file, allowing AI to distinguish voices in multi-speaker environments.
Applications: Call center analytics, speech-to-text applications, media transcription.
Emotion or Sentiment Analysis
Detects emotional tone in speech, determining whether the audio expresses happiness, sadness, anger, or neutrality.
Applications: Customer sentiment tracking, AI-driven chatbots, voice-based emotional intelligence.
Sound Event Detection
Identifies and labels specific sounds such as footsteps, alarms, sirens, or machinery noises, enabling AI to differentiate between various sound sources.
Applications: Security surveillance, environmental sound monitoring, context-aware AI systems.
Music
Annotation
Labels and categorizes music streaming data, helping AI understand genres, instruments, and user preferences.
Applications: Recommendation engines, music categorization, content discovery.
Named Entity Recognition (NER) for Audio
Recognizes and labels spoken entities such as names, locations, organizations, and key topics to improve voice search and AI-powered recommendations.
Applications: News analytics, content indexing, smart search assistants.
Language Identification
Detects and labels the language spoken in an audio file, allowing AI to process multilingual content efficiently.
Applications: AI-driven translation services, voice assistants, cross-lingual customer interactions.
Environmental Sound Annotation
Focuses on labeling background and ambient sounds such as traffic noise, birds chirping, or crowd murmurs, enhancing AI’s ability to contextually analyze surroundings.
Applications: Smart city systems, noise monitoring, environmental AI solutions.