What Is Speech-to-Text and How Does It Work?

What Is Speech-to-Text and How Does It Work?

Speech-to-text uses AI to convert spoken words into written text automatically. Companies use it to turn customer calls, meetings, and consultations into searchable data that flows directly into business systems. Here's how the technology works, where it delivers the most value, and what to consider when choosing a provider.

Speech-to-text (STT), also called automatic speech recognition (ASR) or voice recognition, converts spoken words into text using deep learning models. Some STT systems are even able to do this in real time.

STT has many uses across a range of industries. Healthcare providers can dictate clinical notes while examining patients instead of typing after hours, and contact centers use it to capture every customer conversation without manual transcription work. STT also helps turn voice conversations into searchable data that organizations can analyze for customer pain points, compliance documentation, and conversation patterns that were previously invisible to business systems.

STT processes audio through multiple stages that clean the signal, identify speech patterns, and convert them into text. This infrastructure is very robust and can handle customers with accents or who are calling from noisy environments.

STT systems start by cleaning incoming audio. Noise reduction and volume leveling help filter out background sounds before processing begins. This determines how accurate the final transcript will be.

The system then analyzes the cleaned audio to identify speech patterns and map them to likely words. Modern neural networks can recognize speech variations like accents and room acoustics directly from training data, unlike older methods that struggled with real-world conditions.

Finally, language models determine which words make sense in context. These models understand how words relate to each other across entire sentences, allowing the system to choose "there," "their," or "they're" based on meaning rather than just sound.

Advances in deep learning mean this entire pipeline can process audio in real time while maintaining high accuracy on production calls. However it’s worth noting that these newer systems require more computing power than older approaches.

Depending on the specific enterprise use case, your STT will need to choose from three “modes”:

Synchronous recognition processes complete audio files in one request-response cycle. This works for applications like insurance claim recordings where a brief processing delay won’t impact the workflow.

Streaming recognition transcribes audio as it arrives, delivering tokens in real time. Contact centers and live meeting platforms use this mode because they need to avoid noticeable delays during agent assistance and real-time captions.

Asynchronous recognition handles large audio volumes in background processing. This is perfect for applications like analysing call archives for compliance, where the team would leave the job running for multiple hours at a time.

When selecting an STT mode, aim to match the mode to user expectations: streaming for live conversations, synchronous when simplicity matters more than speed, and asynchronous for high-volume batch processing.

Enterprises that deploy sSTT technology can expect several operational improvements:

• Cost reduction: Automated transcription is significantly cheaper compared to manual services while delivering comparable results.
• Production-grade scale: Deep-learning models can process thousands of concurrent calls without degradation.
• Reliable delivery: High uptimes ensure transcripts arrive when operations need them, even during peak traffic.
• Compliance features: Integrating redaction and other privacy-oriented features contribute to HIPAA and financial regulations
• Real-time insights: Live transcripts enable sentiment analysis during calls and trigger alerts based on conversation content.
• Instant searchability: Because call summaries and meeting notes flow instantly into databases, voice data becomes searchable without manual transcription work.

When operations teams manage thousands of inbound calls, manual QA can cover only a fraction of conversations. However, streaming transcription can capture every word in real time, surface sentiment cues, and flag compliance risks while the customer is still on the line. These transcriptions can be used to provide on-screen prompts for agents, and searchable call logs for supervisors. The result is faster resolution and consistent policy enforcement across every interaction.

Physicians lose hours each day typing notes into electronic health records. With a HIPAA-ready API tuned for medical terminology, clinicians can dictate once and watch structured text flow directly into the EHR. Because the model recognizes drug names, lab values, and abbreviations, physicians will spend less time editing and more time with patients while hospitals create a consistent audit trail for coding and reimbursement.

Regulators expect a verbatim record of client conversations, yet most firms still rely on spot checks. Automated call transcription can capture every meeting, trade confirmation, and claims discussion, then run the text through keyword spotting for phrases that trigger compliance workflows. Instead of sampling 5% of calls, financial institutions monitor all of them.

Selecting a STT provider requires testing with real audio from actual operations. Here’s everything you need to evaluate before making a commitment.

Accuracy matters most when measured with real recordings from your environment. Test with actual audio samples that include background noise, accents, and industry terminology rather than clean demo files. Even small accuracy improvements can significantly reduce the time spent correcting transcripts.

Speed matters for real-time applications like live agent support and voice assistants. Look for providers that maintain fast performance under high call volumes, not just during light usage.

Cloud-based systems offer quick setup, while private or on-premises deployment keeps sensitive audio within your security perimeter. Choose based on regulatory requirements like HIPAA or PCI-DSS if your industry requires it.

Domain-specific terminology and custom vocabulary support can significantly improve accuracy for specialized industries. Test how easily the API recognizes your industry jargon, product names, and technical terms.

Encryption, access controls, and audit logs are all required for most regulatory frameworks. Verify these capabilities during evaluation to avoid discovering limitations after signing contracts.

Compare complete ownership costs, not just per-minute rates. Hidden fees to check for include custom features, support, or high volume pricing. Choose providers that export data in standard formats to maintain flexibility.

Deepgram’s speech-to-text API handles high-volume operations with fast processing, consistent accuracy in challenging conditions, and predictable pricing at scale. Here’s why it’s the best solution for enterprise use cases:

• Low latency performance: Nova-3 STT models are 40x faster than market competitors and maintain that speed through thousands of simultaneous calls. At this capacity, most other APIs queue traffic.
• Real-world accuracy: Deepgram leads the industry with the most accurate models in the market across use case categories, up to 30% more accurate than the competition.
• Flexible deployment: Run fully managed in the cloud, deploy a private tenant, or install on-premises to keep HIPAA or PCI data inside your security perimeter.
• Predictable pricing: Flat per-minute rates with no surcharges for custom vocabularies.
• Enterprise scale: Process thousands of concurrent calls without performance degradation during peak traffic periods.

Deepgram transforms voice data into actionable business intelligence. Organizations using Deepgram can reduce transcription costs, process tens of thousands of concurrent calls, and maintain high accuracy in challenging audio conditions.

Test Deepgram against production audio: sign up for a free Deepgram console account and get $200 in credits to validate performance at scale.