Skip to main content
BVDNET
Arnhem · websites & automationBVDNET
Multimodal & Creative

Speech AI

Speech AI covers technologies for converting speech to text (STT), text to speech (TTS), voice cloning, and speech translation, enabling natural voice interaction with AI.

Also known as: TTS, STT, ASR, automatic speech recognition, text-to-speech, speech-to-text, spraak-AI

Multimodal & CreativeBeginner
2026-W17
AI Intel Pipeline

What is Speech AI?

Speech AI encompasses AI technologies that process and generate human speech, including speech-to-text (STT/ASR), text-to-speech (TTS), voice cloning, and speech translation. These systems enable natural voice interaction between humans and machines.

Why It Matters

Speech AI powers voice assistants (Siri, Alexa, Google Assistant), real-time meeting transcription (Otter.ai, Google Meet), accessibility tools for the visually impaired, podcast production, and the growing trend of voice-first AI interfaces. With GPT-4o and Gemini 2.0 supporting native voice, speech is becoming a primary modality for AI interaction.

How It Works

Speech-to-Text (STT / ASR — Automatic Speech Recognition):

  • Converts spoken audio into written text
  • Modern systems use transformer-based models (e.g., Whisper by OpenAI)
  • Steps: audio preprocessing → feature extraction (mel spectrograms) → sequence-to-sequence model → text output
  • Handles multiple languages, accents, and background noise

Text-to-Speech (TTS):

  • Converts written text into natural-sounding speech
  • Modern systems produce near-human-quality voices
  • Approaches: neural vocoders (WaveNet, VITS), diffusion-based TTS
  • Can be conditioned on speaker identity, emotion, and prosody

Voice cloning:

  • Create a synthetic voice that matches a specific person from minutes or seconds of audio samples
  • Used for personalization, dubbing, and accessibility
  • Raises ethical concerns around consent and deepfakes

Speech translation:

  • Directly translate spoken audio from one language to another
  • Can work end-to-end (speech → speech) or via intermediate text

Native voice AI (emerging):

  • GPT-4o and Gemini process audio natively without STT→LLM→TTS pipeline
  • Lower latency, better understanding of tone and emotion

Example

OpenAI's Whisper model can transcribe a one-hour meeting recording in dozens of languages with near-human accuracy, handling multiple speakers, accents, and background noise. The transcript can then be fed to an LLM for summarization, action item extraction, or translation.

Sources

  1. OpenAI – Whisper
  2. Google Cloud – Speech-to-Text