Audio

Audio and Speech AI encompasses technologies for speech recognition (ASR), text-to-speech synthesis (TTS), and voice-based AI interfaces — using deep learning models to convert speech to text, generate natural-sounding speech, and enable spoken interactions with AI systems, powering voice assistants, transcription services, and multimodal AI applications.

What Is Audio/Speech AI?

  • Definition: AI systems that process, understand, and generate speech/audio.
  • Components: ASR (speech→text), TTS (text→speech), voice AI (end-to-end).
  • Applications: Voice assistants, transcription, dubbing, accessibility.
  • Trend: Integration with LLMs for spoken AI interaction.

Why Audio AI Matters

  • Natural Interface: Voice is the most natural human communication.
  • Accessibility: Enable AI for visually impaired, hands-free contexts.
  • Scale: Voice is primary communication in many cultures.
  • Multimodal AI: Audio is key modality alongside text and vision.
  • Real-Time: Enable live translation, captioning, assistance.

Automatic Speech Recognition (ASR)

Task: Convert spoken audio to text.

Key Models:

Model          | Provider   | Features
---------------|------------|----------------------------------
Whisper        | OpenAI     | Multilingual, robust, open
Wav2Vec2       | Meta       | Self-supervised pretraining
Conformer      | Google     | Hybrid conv + attention
USM            | Google     | Universal speech model
AssemblyAI     | Commercial | Real-time, speaker diarization
Deepgram       | Commercial | Fast, enterprise features

Whisper Architecture:

Speech AI — ASR, TTS, and Voice Cloning waveform → mel spectrogram → encoder → text (ASR) | text → mel → vocoder → waveform (TTS) ASR — Speech to Text Waveform 16kHz PCM Mel Spec 80 bins × T Encoder Transformer Decode Whisper 680K hrs, 99 langs, seq2seq Wav2Vec2 self-supervised + CTC Conformer conv + attention (Google) Canary (NVIDIA) multilingual, fast TTS — Text to Speech Text Acoustic Model text → mel/codec Vocoder HiFi-GAN Audio VALL-E / XTTS codec-based, voice clone from 3s Bark GPT-style, multilingual + effects Tortoise diffusion, high quality, slow F5-TTS flow-matching, zero-shot voice Neural Audio Codecs Modern TTS uses discrete audio tokens (not mel): EnCodec (Meta) RVQ → 8 codebooks × 75 Hz SoundStream (Google) similar RVQ architecture DAC improved codebook utilization Deployment Considerations Real-time factor (RTF): must be < 1.0 for streaming Latency: first-byte < 200ms for conversational Streaming: chunk-wise ASR (Whisper-streaming) Edge: on-device ASR (Whisper-tiny: 39M params) Voice AI Applications Voice Assistants Siri, Alexa, GPT-4o voice Voice Cloning 3-second reference → clone Transcription meetings, podcasts, medical Translation speech-to-speech (SeamlessM4T) Music MusicGen, Suno The shift: mel spectrograms → discrete audio tokens (codecs) lets LLMs generate speech like text — token by token. Speech AI converged on the transformer: Whisper proved one model can transcribe 99 languages at human parity.

Text-to-Speech (TTS)

Task: Generate natural speech from text.

Key Models:

Model          | Provider   | Features
---------------|------------|----------------------------------
XTTS           | Coqui      | Zero-shot voice cloning, open
VITS           | Research   | End-to-end, high quality
Bark           | Suno       | Expressive, non-speech sounds
StyleTTS 2     | Research   | Style control, prosody
ElevenLabs     | Commercial | Best quality, voice cloning
PlayHT         | Commercial | Realistic, streaming

TTS Pipeline:

Text Input: "Hello, how are you?" ↓┌─────────────────────────────────┐│ Text Processing ││ - Normalization, phonemization │├─────────────────────────────────┤│ Acoustic Model ││ - Generate mel spectrogram ││ - Control prosody, duration │├─────────────────────────────────┤│ Vocoder ││ - Convert spectrogram to audio ││ - HiFi-GAN, WaveGrad │└─────────────────────────────────┘ ↓Audio Output (wav/mp3)

Voice Cloning

Zero-Shot Cloning:
- 3-30 seconds of reference audio.
- Model generates speech in that voice.
- XTTS v2, ElevenLabs, PlayHT.

Fine-Tuned Cloning:
- Train on hours of target speaker.
- Higher quality, more customization.
- More compute and data required.

Evaluation Metrics

ASR Metrics:
- WER (Word Error Rate): (S+D+I)/N — lower is better.
- CER (Character Error Rate): Character-level WER.
- Real-Time Factor: Processing time / audio duration.

TTS Metrics:
- MOS (Mean Opinion Score): Human rating 1-5.
- WER on ASR: Transcribe generated speech, measure errors.
- Speaker Similarity: Compare to reference voice.

Voice AI Assistants

Architecture:

User Speech ↓┌─────────────────────────────────┐│ ASR: Speech → Text │├─────────────────────────────────┤│ LLM: Understand + Generate │├─────────────────────────────────┤│ TTS: Text → Speech │└─────────────────────────────────┘ ↓Assistant Response (audio)

Emerging: GPT-4o Style:
- Native audio tokens in LLM.
- No separate ASR/TTS pipeline.
- Lower latency, better prosody.

Tools & Frameworks

  • Whisper: OpenAI's open ASR model.
  • Coqui TTS/XTTS: Open TTS with voice cloning.
  • Hugging Face: ASR/TTS pipeline support.
  • faster-whisper: Optimized Whisper inference.
  • RealtimeSTT/TTS: Real-time streaming libraries.

Audio and Speech AI is enabling natural spoken interfaces to AI — as voice becomes a primary way to interact with AI systems, speech technology forms the essential bridge between human communication and machine intelligence.

Take audio further

Ask the copilot about this term, or have our engineers assess it against your process.

Ask CFSGPT about this → Request a technical assessment → Open the CFS app