Speech-to-Text (ASR)

What Is Speech-to-Text (ASR)?

Speech-to-text (STT), also known as automatic speech recognition (ASR), is the technology that converts spoken language into written text in real time. In voice AI customer service, ASR is the first step in the pipeline: the customer speaks, ASR converts their words to text, the AI agent processes the text to understand intent and formulate a response, and then text-to-speech converts the response back to spoken words.

Modern ASR systems use deep learning models trained on millions of hours of speech to achieve high accuracy across accents, languages, and noisy environments.

How ASR Works in Customer Service

When a customer calls a support line powered by voice AI, the ASR system:

1. Captures the audio stream from the phone call
2. Segments the audio into processable chunks
3. Converts each segment to text using neural network models
4. Applies language models to improve accuracy (correcting "I need a re-fund" to "I need a refund")
5. Passes the transcribed text to the AI agent for intent recognition and processing

This entire process happens in real time — modern systems target latency under 250 milliseconds to maintain natural conversation flow.

Market context: The voice AI agent market was valued at $3.5 billion in 2025 and is projected to reach $28.9 billion by 2033 (30.1% CAGR). Real-time voice agent deployments scaled 4x in 2025, with contact centers preparing infrastructure for 39 billion calls by 2029.

ASR Challenges and Accuracy

Enterprise ASR achieves speech-recognition error rates below 5% in ideal conditions, but real-world accuracy varies with:

• Background noise: Call center environments, speakerphones, and public locations reduce accuracy
• Accents and dialects: Accuracy can vary significantly across speaker demographics
• Domain vocabulary: Industry-specific terms (medical, financial, technical) require specialized models
• Audio quality: Poor phone connections or low-quality microphones degrade results

The Maven Advantage: Voice AI That Understands

Maven Voice integrates with leading ASR providers and voice engines including OpenAI, Phonic, Cartesia, and ElevenLabs to deliver high-accuracy speech recognition across languages. Maven's voice pipeline handles interruptions naturally, maintains natural conversation pacing, and supports real-time PII redaction on both audio and text transcripts — ensuring sensitive information shared by phone is protected.

Maven proof point: Maven Voice connects to existing telephony infrastructure via Twilio, RingCentral, Cisco, Genesys, and Zendesk Talk, supporting SIP, PSTN, and WebRTC — meaning organizations can add voice AI without replacing their phone systems.

Frequently Asked Questions

What's the difference between ASR and NLP?

ASR converts speech to text — it handles the audio-to-text conversion. NLP processes that text to understand meaning, intent, and context. ASR answers "what did the customer say?" while NLP answers "what did the customer mean?"

Can ASR handle multiple languages in the same conversation?

Modern multilingual ASR models can detect and transcribe multiple languages, including mid-conversation language switches. This is increasingly important for global customer service operations supporting diverse customer bases.

Does ASR accuracy affect AI agent performance?

Directly. If ASR misinterprets "refund" as "re-fund" or "cancel" as "council," the AI agent receives incorrect input and may produce an irrelevant response. High-quality ASR is foundational to effective voice AI customer service.

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