Who should use the Build a Voice-Controlled Assistant with Speechly workflow?
Teams or solo builders working on voice & speech tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Voice & Speech
Create a real-time voice interface for applications using Speechly's ASR and NLU to transcribe and interpret user commands with low latency.
Deliverable outcome
A robust, low-latency voice assistant that handles errors gracefully and works in varied conditions.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A robust, low-latency voice assistant that handles errors gracefully and works in varied conditions.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Speechly to speechly client is initialized and ready to receive audio input. Then, you pass the output to Speechly to a custom nlu model that recognizes your specific voice commands and extracts parameters. Then, you pass the output to Speechly to microphone audio is captured and streamed to speechly in real time. Then, you pass the output to Speechly to voice input is converted into structured intent and entity data in real time. Then, you pass the output to Speechly to voice commands trigger real actions and ui updates in your application. Finally, Speechly is used to a robust, low-latency voice assistant that handles errors gracefully and works in varied conditions.
Set Up Speechly Project and Client Credentials
Speechly client is initialized and ready to receive audio input.
Define Voice Commands and NLU Model
A custom NLU model that recognizes your specific voice commands and extracts parameters.
Implement Microphone Access and Audio Streaming
Microphone audio is captured and streamed to Speechly in real time.
Handle Real-Time Transcription and Intent Callbacks
Voice input is converted into structured intent and entity data in real time.
Integrate Voice Commands with Application UI and Logic
Voice commands trigger real actions and UI updates in your application.
Optimize for Low Latency and Error Handling
A robust, low-latency voice assistant that handles errors gracefully and works in varied conditions.
Create a Speechly account and a new application in the Speechly dashboard. Obtain the App ID and API credentials, then install the Speechly client library in your project (e.g., via npm for React or a CDN for vanilla JS). This step establishes the foundational connection between your code and Speechly's cloud services.
Why Speechly: Speechly provides the dashboard and client libraries needed to set up the project and obtain credentials for voice-controlled assistant development.
In the Speechly Dashboard, write a YAML configuration file that defines intents (e.g., 'openApp', 'setTimer') and entities (e.g., 'appName', 'duration'). Train the NLU model by uploading sample phrases. This step translates your application's functionality into a machine-understandable voice schema.
Why Speechly: Speechly's dashboard allows defining intents, entities, and voice commands via YAML configuration, which is exactly what this step requires.
Write code to request the user's microphone permission and stream audio to Speechly's ASR engine. Use the Speechly client's start/stop methods to control recording. This step ensures real-time audio capture with low latency.
Why Speechly: Speechly's client library provides built-in microphone access and audio streaming capabilities for real-time voice processing in the browser.
Attach event listeners to the Speechly segment for 'transcript', 'intent', and 'entity' events. Parse the incoming data to extract the final transcript and structured command objects. This step bridges raw audio output to actionable data.
Why Speechly: Speechly's client event system provides real-time transcription callbacks and intent events, which is the core requirement for handling voice recognition results.
Write a command dispatcher that maps each recognized intent to a specific function (e.g., 'openApp' → navigate to page, 'setTimer' → start countdown). Update the UI state based on voice commands, such as showing a confirmation message or changing a visual element. This step makes the assistant actually control your app.
Why Speechly: Speechly provides voice command interpretation for UI actions, enabling direct integration of voice commands with application state and UI components.
Adjust Speechly client settings like 'sampleRate' and 'noiseSuppression' to improve accuracy. Implement fallback logic for unrecognized commands (e.g., 'Sorry, I didn't understand'). Test with various accents and background noise levels. This step polishes the user experience.
Why Speechly: Speechly's real-time processing capabilities and client configuration options allow for latency optimization and error handling in voice recognition.
§ Before you start
Teams or solo builders working on voice & speech tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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