Streaming ASR Architecture
Why batch ASR won’t work for voice assistants, and how streaming models transcribe speech as you speak in under 200ms.
Speech Technology
Technical deep-dives into speech and audio ML systems, from research foundations to production deployment. Covers ASR, TTS, speaker recognition, and conversational AI with both theoretical insights and practical engineering.
Each post includes:
- Architecture and model design choices
- Streaming and real-time processing considerations
- Research foundations and recent advances
- Production deployment and optimization strategies
- Scaling, cost analysis, and performance tuning
- Code examples and implementation details
Browse by Topic
Automatic Speech Recognition (ASR):
Speech Classification:
Audio Processing:
- Audio Feature Extraction
- Voice Activity Detection
- Audio Preprocessing & Signal Processing
- Voice Enhancement & Noise Reduction
Real-time Processing:
Text-to-Speech (TTS):
Speaker Technology:
Speech Separation:
Conversational AI:
- Coming soon…
Model Optimization:
- Coming soon…
Speech Tech Index
Below you’ll find all speech technology posts in chronological order:
Content created with the assistance of large language models and reviewed for technical accuracy.
Speech Command Classification
How voice assistants recognize “turn on the lights” from raw audio in under 100ms without full ASR transcription.
Audio Feature Extraction for Speech ML
How to transform raw audio waveforms into ML-ready features that capture speech characteristics for robust model training.
Voice Activity Detection (VAD)
How voice assistants and video conferencing apps detect when you’re speaking vs silence, the critical first step in every speech pipeline.
Speaker Recognition & Verification
How voice assistants recognize who’s speaking, the biometric authentication powering “Hey Alexa” and personalized experiences.
Text-to-Speech (TTS) System Fundamentals
From text to natural speech: understanding modern neural TTS architectures that power Alexa, Google Assistant, and Siri.
Audio Preprocessing & Signal Processing
Clean audio is the foundation of robust speech systems, master preprocessing pipelines that handle real-world noise and variability.
Streaming Speech Processing Pipeline
Build real-time speech processing pipelines that handle audio streams with minimal latency for live transcription and voice interfaces.
Real-time Keyword Spotting
Build lightweight models that detect specific keywords in audio streams with minimal latency and power consumption for voice interfaces.
Voice Enhancement & Noise Reduction
Build systems that enhance voice quality by removing noise, improving intelligibility, and optimizing audio for speech applications.
Speech Separation
Separate overlapping speakers with 99%+ accuracy: Deep learning solves the cocktail party problem for meeting transcription and voice assistants.
Multi-Speaker ASR
Build production multi-speaker ASR systems: Combine speech recognition, speaker diarization, and overlap handling for real-world conversations.
Compute Allocation for Speech Models
Optimize speech pipeline throughput by allocating compute to bottleneck stages using greedy resource management.
Multi-model Speech Ensemble
Build production speech systems that combine multiple ASR/TTS models using backtracking-based selection strategies to achieve state-of-the-art accuracy.