Speech Recognition Explained: How ASR Technology Powers Real-Time Voice AI

While text-to-speech gives AI agents their voice, speech recognition gives them their ears. Automatic Speech Recognition (ASR)—also called Speech-to-Text (STT)—is the technology that transforms spoken words into text that AI can understand and act upon. For voice-controlled product demos and conversational AI, the quality of speech recognition directly determines user experience.

At Demogod, we have built voice AI agents that guide users through websites in real-time. The speech recognition layer is critical—it must be fast, accurate, and able to handle diverse accents, background noise, and domain-specific vocabulary. Here is everything you need to know about modern ASR technology.

How Speech Recognition Works

Modern ASR systems convert audio waveforms into text through several stages:

Audio Preprocessing

Raw audio is cleaned and normalized. This includes noise reduction, echo cancellation, and converting to the optimal sample rate (typically 16kHz for speech). Voice Activity Detection (VAD) identifies when someone is speaking versus silence or background noise.

Feature Extraction

The audio is converted into numerical features. Traditional systems used Mel-frequency cepstral coefficients (MFCCs), while modern neural networks often work directly with spectrograms—visual representations of audio frequencies over time.

Acoustic Modeling

Deep neural networks (typically transformer-based architectures) process the audio features to identify phonemes—the smallest units of sound in speech. These models are trained on thousands of hours of transcribed audio.

Language Modeling

Context from language models helps resolve ambiguities. "Ice cream" vs "I scream" sound identical, but language models use context to determine the correct transcription. Modern end-to-end models combine acoustic and language modeling in a single neural network.

Key ASR Providers Compared

Choosing the right ASR provider impacts latency, accuracy, and cost. Here is how the major players compare:

Deepgram

Best for: Real-time applications requiring low latency

Latency: Under 300ms for streaming

Accuracy: 90-95% on clean audio, with Nova-2 model

Pricing: $0.0043/minute (Pay-as-you-go)

Strengths: Purpose-built for developers, excellent streaming API, custom vocabulary support, speaker diarization

Weaknesses: Smaller language coverage than Google

OpenAI Whisper

Best for: Batch transcription, multilingual content

Latency: Not designed for real-time (batch processing)

Accuracy: 95%+ on clean audio, excellent multilingual

Pricing: $0.006/minute (API) or free (self-hosted)

Strengths: Open-source, 99 languages, robust to accents, can self-host

Weaknesses: Higher latency, requires chunking for long audio

Google Cloud Speech-to-Text

Best for: Enterprise applications, phone call transcription

Latency: 200-400ms streaming

Accuracy: 92-97% depending on model

Pricing: $0.006-0.009/minute

Strengths: 125+ languages, medical/phone call models, robust infrastructure

Weaknesses: More complex pricing, steeper learning curve

Amazon Transcribe

Best for: AWS ecosystem users, call center analytics

Latency: 200-500ms streaming

Accuracy: 90-95%

Pricing: $0.024/minute (standard), $0.015/minute (batch)

Strengths: AWS integration, custom vocabulary, PII redaction

Weaknesses: Higher cost, fewer language options

AssemblyAI

Best for: Content creators, podcast transcription

Latency: Real-time streaming available

Accuracy: 93-97% (Universal-2 model)

Pricing: $0.00025/second (~$0.015/minute)

Strengths: LeMUR for audio intelligence, speaker labels, content safety

Weaknesses: Newer player, smaller enterprise footprint

Accuracy Benchmarks: What the Numbers Mean

ASR accuracy is measured by Word Error Rate (WER)—the percentage of words incorrectly transcribed. Lower is better:

Human transcription: 4-5% WER
Best AI systems: 5-8% WER on clean audio
Phone call audio: 10-15% WER typical
Noisy environments: 15-25% WER

However, WER benchmarks can be misleading. A system with 5% WER on LibriSpeech (clear audiobook recordings) might have 20% WER on real-world audio with accents, crosstalk, and background noise. Always test on audio similar to your production use case.

Real-Time vs Batch Processing

For voice AI agents, real-time streaming is non-negotiable. Users expect immediate responses—any delay feels unnatural.

Streaming ASR Requirements

Latency under 500ms: From speech end to text availability
Interim results: Show partial transcriptions as user speaks
Endpoint detection: Know when user finished speaking
Barge-in support: Let users interrupt the AI agent

Batch processing works for transcribing recordings but cannot power interactive voice experiences. When building voice AI for product demos, streaming ASR with low latency is essential.

Handling Real-World Challenges

Background Noise

Users interact with voice AI in offices, cars, coffee shops. Modern ASR systems use noise-robust models trained on augmented data with synthetic noise. Preprocessing with noise suppression (like Krisp or RNNoise) improves accuracy significantly.

Accents and Dialects

Global products need ASR that handles diverse accents. Whisper excels here due to training on multilingual data. Some providers offer accent-specific models or allow fine-tuning on your user demographic.

Domain Vocabulary

Technical products have jargon ASR models have never seen. Custom vocabulary features let you boost recognition of product names, technical terms, and company-specific language. "Demogod" might transcribe as "demo god" without custom vocabulary hints.

Homophones and Context

"Their," "there," and "they are" sound identical. Language model integration helps, but for critical applications, showing users their transcribed text and allowing corrections improves accuracy.

The Voice AI Pipeline

Speech recognition is one component of a complete voice AI system:

Audio Capture: WebRTC for browser-based voice
Speech Recognition: Convert audio to text (this article)
Natural Language Understanding: Extract intent from text
Dialog Management: Determine appropriate response
Text-to-Speech: Generate spoken response
Audio Playback: Deliver response to user

The entire round-trip must complete in under 2 seconds for natural conversation. ASR typically takes 200-500ms of this budget.

Implementing ASR: Best Practices

Choose Streaming from Day One

Retrofitting batch ASR into a real-time system is painful. Design for streaming even if you start with simpler use cases.

Handle Errors Gracefully

ASR will make mistakes. Design your voice UI to handle misrecognition—confirmation prompts, easy corrections, and fallback to text input.

Monitor Accuracy in Production

Log transcriptions and sample regularly. Accuracy on your real users may differ significantly from benchmarks.

Consider Privacy

Audio data is sensitive. Understand data retention policies and consider on-premise options for healthcare, finance, or other regulated industries.

The Future: Multimodal and Contextual ASR

Next-generation ASR systems will use visual context (lip reading, gestures) and situational awareness to improve accuracy. Models like Gemini already combine audio and visual understanding. For voice AI agents navigating websites, future systems might use DOM context to better understand what users are asking about.

Speech recognition has evolved from frustrating voice menus to near-human accuracy. Combined with advances in TTS and language models, we are entering an era where voice becomes the most natural way to interact with software.

See Voice AI in Action

Want to experience how speech recognition powers real-time voice AI? Try Demogod—our voice agents use state-of-the-art ASR to understand your questions and guide you through product demos naturally. Speak, and the AI listens, understands, and responds in real-time.

The technology that once seemed like science fiction is now powering the next generation of product experiences. Voice AI is not coming—it is here.