AI core

Transcription:
voice to text

Level AI’s speech-to-text engine converts billions of spoken words into accurate transcripts each month. It's powered by a proprietary seven-stage AI pipeline and dedicated audio servers for real-time performance.

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Models

Seven core models

Voice Activity Detection (VAD)

Filters speech from noise and silence, focusing only on spoken words.

Acoustic Model

Identifies basic sounds and letters spoken, the building blocks of words.

Language model

Arranges sounds into naturally flowing sentences.

Profanity detection

Checks for and removes inappropriate language.

Speaker diarization

Identifies and tracks when different speakers talk in a conversation.

Punctuation model

Adds correct punctuation and capitalization for easy-to-read text.

Inverse text normalization

Formats numbers and special terms (three and a half dollars -> $3.50).

Speech recognition pipeline in action

Level AI's speech recognition pipeline turns raw transcription text output into a readable, professionally formatted document.

“As a design and marketing partner to millions of small businesses worldwide, Vista has always prioritized customer experience. Level AI’s agent screen recording has added “eyes” to a process where we only had “ears” before. This has helped us identify opportunities to improve our processes and tools and coach our agents more effectively, which improves both team member satisfaction and customer experience.”

Michael Villanueva

Global Director of Quality - Vista

Michael Villanueva

Global Director of Quality - Vista

Michael Villanueva

Global Director of Quality - Vista

Recognition

Convertion

Understanding

Learning

Voice recognition

Voice Activity AI precisely identifies speech and filters out background noise.

Speaker separation: Differentiates each speaker’s voice.
Speech detection: Distinguishes speech from silence.
Timestamp accuracy: Marks precise start and end points of speech.

How speech becomes text

This system listens to spoken words, identifying the individual sounds and letters that form them.

Audio intake: The system receives and prepares the raw sound.
Acoustic clues: Detects patterns in pitch, volume, and rhythm.
Text prediction: Clues are translated to letters and words.

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.

Sound guesses: The system's initial phonetic predictions.
Expert interpretation: Inferred guesses by the Language model.
Final text: The words the system understood.

The self-learning cycle

The AI learns by listening, interpreting, and generating its own practice exercises, improving continuously without human input and learning efficiently from all available audio.

Audio ingestion: Listens to and processes audio.
Comprehension: Interprets intent and meaning.
Auto-practice: Generates its own training sessions.
Continuous learning: Improves accuracy over time.

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Transcription:
voice to text

Seven core models