AssemblyAI

What is AssemblyAI?

AssemblyAI provides Speech AI models for converting spoken audio into text and extracting insights from voice data. The website highlights streaming speech-to-text capabilities and model prompts/configurations designed to capture more than just plain transcripts—such as disfluencies, speaker roles, key terms, audio tagging cues, and code-switching.

The product is positioned for teams building voice applications, including voice agents. The site also references documentation resources like real-time transcription and a LiveKit SDK to help developers integrate speech processing into voice workflows.

Key Features

• Streaming speech-to-text for real-time voice agents: Designed to transcribe continuously as speech is produced, supporting voice-agent workflows rather than batch-only processing.
• Context-aware prompting: Prompts can be tailored to preserve details such as medication dosage accuracy and to include specific transcript elements (e.g., fillers, repetitions, restarts, stutters, and informal speech).
• Disfluency capture (spoken “hesitations” and interruptions): Examples show producing transcripts that retain fillers (e.g., “um,” “uh”), repetitions, restarts, and stutters for conversational or clinical-style analysis.
• Audio tagging for non-speech events: Prompts can request tags for events such as system sounds (e.g., a “beep”) to preserve important non-verbal or signaling information.
• Speaker-role labeling: Prompts can require labeling each speaker turn with roles (e.g., “NURSE,” “PATIENT”) to structure multi-speaker conversations.
• Keyterm extraction/spelling control: The site includes examples where key terms (e.g., proper noun spelling like “Kelly Byrne-Donoghue”) are handled via prompts.
• Language detection and code-switching support: Examples show preserving language as-is when speakers switch between English and Spanish.

How to Use AssemblyAI

1. Choose a speech workflow such as real-time transcription or a voice-agent flow (the site references real-time transcription documentation and a LiveKit SDK).
2. Select the output you need for your transcript: plain text, or structured outputs that include disfluencies, non-speech audio tags, speaker roles, key terms, or code-switching.
3. Use prompts/configuration examples to request the transcript format and level of detail relevant to your use case (e.g., medication-focused clinical histories vs. conversational analysis).

Use Cases

• Voice-agent conversation transcription with detailed speaking behavior: Produce transcripts that include fillers, repetitions, restarts, and stutters for downstream conversational analysis.
• Clinical history-style transcription that preserves medication details: Generate transcripts where medication names and dosages are captured accurately and disfluencies are retained as meaningful data.
• Call or IVR transcription with audio event tagging: Include tags for non-speech events such as system prompts or beeps so transcripts reflect the signaling in the audio.
• Multi-speaker interviews with role attribution: Label each turn with a speaker role (e.g., nurse vs. patient) to structure transcripts for review or documentation.
• Bilingual conversations where language switches mid-sentence: Preserve spoken language patterns during English/Spanish code-switching rather than normalizing everything to one language.

FAQ

• Does AssemblyAI support real-time transcription for voice agents? The site highlights streaming speech-to-text intended for voice-agent workflows and references “real-time transcription” resources.

• Can the transcript include more than plain text? Yes. The examples show prompts requesting disfluencies, non-speech audio tags, proper-noun/keyterm handling, speaker-role labeling, and code-switching preservation.

• How is disfluency handled in transcripts? The website shows examples where prompts instruct the model to include fillers, repetitions, restarts, and stutters in the transcript.

• Can speaker roles be included in the output? The site includes an example requesting speaker turns labeled with roles (e.g., “Speaker [Nurse]”, “Speaker [Patient]”).

• Is language detection and code-switching supported? The site includes examples indicating language detection and preserving natural English/Spanish code-switching.

Alternatives

• Speech-to-text APIs from other cloud providers: These typically offer streaming transcription and diarization-like features, but may vary in how reliably they preserve disfluencies, audio-event tags, or structured prompt-driven outputs.
• Open-source speech recognition toolkits: Useful if you want self-hosted transcription, though you may need additional work to reproduce the prompt-driven formatting (disfluencies, speaker roles, code-switching preservation) shown on AssemblyAI’s site.
• Voice-agent platforms with built-in transcription: Some platforms integrate transcription directly into agent frameworks; compare how configurable their transcript formatting is and whether they support the same transcript elements (e.g., disfluencies and tagging).
• General-purpose audio-to-text pipelines (batch transcription tools): Often better suited for recorded/batch files; you may need different tooling for real-time, voice-agent use cases highlighted for AssemblyAI.