(list) - AI-powered voice synthesis and speech restoration

1. Research Projects & Academic Initiatives

A. Voice Reconstruction & Synthesis

• Voiceitt (Technion, Israel)

  • Focus: AI for atypical speech recognition (e.g., dysarthria).

  • Tech: Combines ASR (Automatic Speech Recognition) with personalized ML models.

  • Website

• VocaliD (Northeastern University + Speech Technology Lab)

  • Focus: Custom synthetic voices using minimal speech samples.

  • Tech: Blends donor voice banks with AI to create unique vocal identities.

  • Research Paper

• Google’s Project Relate

  • Focus: Speech recognition for impaired speakers (ALS, Parkinson’s).

  • Tech: LLM fine-tuning for non-standard speech patterns.

  • Website

B. Text-to-Speech (TTS) & Voice Cloning

• OpenAI’s Voice Engine

  • Focus: High-fidelity voice cloning from short samples.

  • Tech: GPT-4 + diffusion models for expressive, natural speech.

  • Blog Post

• Meta’s Voicebox

  • Focus: Generative speech models for restoration.

  • Tech: Non-autoregressive LLMs for real-time voice synthesis.

  • Research Paper

• Microsoft’s VALL-E X

  • Focus: Zero-shot multilingual TTS for voice preservation.

  • Tech: LLM-based prosody and accent transfer.

  • GitHub

2. Companies & Startups

A. Speech Restoration for Medical Conditions

• Acapela Group (Acapela Voice)

  • Focus: Personalized TTS for speech disabilities.

  • Product: "My Own Voice" for laryngectomy patients.

  • Website

• Lyrebird AI (acquired by Descript)

  • Focus: Voice cloning for assistive communication.

  • Tech: Deep learning for synthetic voice replication.

  • Website

• Whisper (OpenAI’s ASR + Custom TTS)

  • Focus: Real-time speech-to-text for impaired speakers.

  • Use Case: Integrates with AAC (Augmentative & Alternative Communication) devices.

B. Next-Gen Voice Assistants & Augmented Communication

• Deepgram

  • Focus: Real-time speech recognition + LLM-powered responses.

  • Use Case: Voice interfaces for motor-impaired users.

  • Website

• ElevenLabs

  • Focus: Hyper-realistic AI voices with emotional control.

  • Tech: LLM-driven prosody adaptation.

  • Website

• Cerence

  • Focus: AI-powered voice banking for neurodegenerative diseases.

  • Product: "Cerence Voice" for preserving natural speech.

  • Website

3. Emerging Directions (2024–2025)

• Neural Prosthetics for Speech (e.g., Brain-Computer Interfaces):

  • Synchron & Neuralink: Decoding neural signals into speech via LLMs.

• Emotion-Aware TTS:

  • Companies like Resemble AI adding emotional layers to synthetic voices.

• On-Device LLMs (e.g., Apple’s AI for AAC):

  • Privacy-focused real-time speech synthesis.

Key Challenges

• Data Bias: Most LLMs are trained on normative speech, struggling with impairments.

• Latency: Real-time synthesis for conversational use remains hard.

• Ethics: Voice cloning risks (consent, deepfakes).