Voice AI
Transcription : Voice to Text
Our speech-to-text technology accurately converts billions of spoken words into written text every month. It uses specialized analysis to ensure every word is understood and transcribed correctly.
Seven Core Models
Filters speech from noise and silence, focusing only on spoken words.
Identifies basic sounds and letters spoken, the building blocks of words.
Arranges sounds into understandable, naturally flowing sentences.
Checks for and removes inappropriate language in transcribed text.
Identifies and tracks when different speakers talk in a conversation.
Adds correct punctuation and capitalization for easy-to-read text.
Formats numbers and special terms professionally (e.g., "three and a half dollars" becomes "$3.50").
Voice Activity Detection (VAD)
VAD acts as a smart filter, identifying exactly when someone is speaking and ignoring background noise. It ensures only actual speech is analyzed, making our systems faster and more accurate for quick, reliable results.
- Separates each speaker's voice
- Distinguishes speech from silence
- Marks the precise start and end of spoken parts
How Speech Becomes Text
This system listens to spoken words, identifying the individual sounds and letters that form them.
- Hearing the Voice: First, audio is prepared for analysis, much like your ear receiving sound waves.
- Finding Sound Clues: Next, key sound characteristics like pitch, volume, and rhythm are identified.
- Guessing the Letters: Finally, these sound clues are used to predict letters and words, forming meaningful text.
Making Sense of Your Speech
Each client has a custom language model. Trained on their specific conversations, it learns the unique words and phrases of their industry and business, making it system more accurate in understanding their vocabulary.
- Sound Guesses: The system's initial phonetic predictions.
- Expert Interpretation: Language model interprets these guesses.
- Final Text: The words the system understood.
The Self-Learning Cycle
Our AI learns by listening to audio, interpreting content, and then generating its own practice exercises. This continuous cycle rapidly improves its understanding without human input.This method allows the AI to learn efficiently from all available audio, even un-labeled data, significantly boosting its performance.
- Listen to Audio
- AI Tries to Understand
- Creates its own Practice sessions
- AI Learns & Improves
Where Context Meets Accuracy
