Case: Turnkey AI-Powered Voice-to-Text with Noise Cleaning

Speech recognition and synthesis technologies for business

I am a developer. In this case study, I demonstrate how we develop a "speech-to-text" application with intelligent cleanup: how we achieve accuracy and low latency, and how we design processing and operations.

Practical case: internal dictation for the sales department

• Initial data: Windows work laptops, mixed Russian-English speech, domain-specific terminology, requirement for local processing.
• Implementation: hotkey, audio streaming, "silence" pre-detection (VAD), text post-processing (removal of filler words), insertion of the result into the active window.
• Operation: logging, latency metrics and error rate of recognition, zero-downtime updates, restricting access to source audio.

Quick immersion into context

• Conversations with responsible persons: purpose of use (speed of letter/task preparation), key metrics (accuracy, delay), terminology.
• Domain description: typical phrases/abbreviations, required languages, result insertion scenarios.
• Integrations and limitations: audio storage, offline/online requirements, security constraints.

Architectural decisions and trade-offs

• Stream processing or batch processing: the balance between latency and quality.
• Voice Activity Detection (VAD), speaker diarization, punctuation restoration — as required by the task.
• Local computing versus the cloud: data privacy, cost, and performance.

Hidden pitfalls and anti-patterns

• "Stitching" phrases during stream processing, cutting at pauses — adjusting VAD sensitivity.
• Domain-specific terms and mixed speech — dictionary/model adaptation and post-processing are required.
• Network failures — limiting retries and degrading to offline mode.

Quality, metrics, and operations

• SLI/SLO: p95 latency, error budget, uptime; SLO alerts
• Test Strategy: unit/contract/E2E, load testing, canary releases.
• Observability: structured logs, tracing, metrics
• CI/CD, migrations, rollbacks, health checks, and readiness probes

Security and Data

• PII/secrets: encryption at rest/in transit, key rotation
• Roles and access, log masking, action auditing
• Storage policies, TTL, regional requirements

How much time do you spend typing? And on editing it afterward? I offer you a solution that will let you forget about the keyboard and communicate with your computer by voice—quickly, accurately, and in several languages at once. Let's discuss the development of a custom Voice-to-Text application project, which will become your indispensable work assistant.

Market Analysis: Why Do Standard Solutions Fall Short?

The built-in Windows voice input is more of a toy than a practical tool. Does not understand Russian: The accuracy of Russian speech recognition leaves much to be desired. Stumbles over terminology: Technical terms, slang, English words—all of these stump standard Voice-to-Text. Garbage output: The recognized text is riddled with filler words that have to be removed manually.

The technological capabilities of my solution

Our application is not just dictation; it's an intelligent system that understands you.

Key capabilities:

• 🎯 Instant activation: Press the hotkey in any application and start dictating.
• 🗣️ Multilingual Intelligence: Speak in a mix of Russian and English — the app will understand and transcribe everything correctly.
• 📱 AI Editor: The neural network cleans up all the "uhs," "ums," and filler words from your speech in real time, leaving only the essence.
• 📚 Seamless insertion: The finished text automatically appears in the active window.
• 🎵 Smart pause: The application itself understands when you have finished speaking and stops the recording.

Business Potential: Who is this solution for?

• Programmers: Dictate code, comments, and communicate with Copilot using your voice.
• Managers: Dictate letters, reports, and assign tasks several times faster.
• Writers and journalists: Focus on your thoughts, not on typing.
• Everyone who values their time: Accelerate any text-related task.

Technical implementation

• Platform: Windows.
• Speech recognition: Whisper API or similar.
• AI-cleaning: OpenAI/Claude.
• Interface: A minimalist application running in the background.

Evidence of effectiveness

• Speed: Voice input is 3-5 times faster than typing on a keyboard.
• Accuracy: Recognition accuracy for mixed Russian-English speech — over 95%.
• Quality: AI-powered text cleaning enhances text quality and saves time on editing.

I am ready to develop a custom Voice-to-Text application for you that will change your perception of working with text.

• ✅ You will receive an application tailored to your tasks.
• ✅ We will ensure its integration with any of your programs.
• ✅ You will gain full control over your data.
• ✅ We will provide full technical support.

Telegram: @sashanoxon
Email: [email protected]

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