To add speech recognition to a Node.js voice assistant, capture or load audio, send it to a recognizer configured for that audio’s format and language, then pass the returned transcript to your intent or dialogue logic. For live microphone input, use a streaming recognition path; for an existing recording, a one-shot transcription request may be enough. Speech recognition supplies text—it does not, by itself, create a complete conversational assistant or make the assistant speak.
Choose hosted or offline recognition
Your first choice is where recognition runs. Google Cloud Speech-to-Text offers a Node.js client and documented microphone-streaming example. The Google quickstart prerequisites include a Cloud project, API enablement, billing, and local authentication; the example uses Application Default Credentials. Follow Google’s current setup instructions rather than putting a service-account secret in application source code.
To install the client library, Google’s Node.js client reference gives this command:
npm install @google-cloud/speechThe reference describes the library as stable. Check the current client documentation for API versions and setup details before deployment: Google Cloud Speech-to-Text Node.js client reference.
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For local, offline recognition, Vosk is an open-source alternative with Node.js bindings. Its project README describes offline and streaming recognition, small models, and virtual assistants as a use case. Those are project capabilities, not an independent comparison or accuracy benchmark. Check the specific Node package, model, language support, and runtime compatibility for your target system; the README directs users to the Vosk site for installation instructions: Vosk API project.
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Get microphone audio into Node.js
For a live assistant, microphone capture and recognition are separate stages. Google’s Node.js streaming example uses node-record-lpcm16 with @google-cloud/speech; use it as a starting point for wiring captured audio to a recognition stream, not as a guarantee that the same capture setup works identically on every operating system. Check the capture package and audio device on the platform you will ship.
You can use a built-in microphone or an existing compatible external microphone; the sample does not require a particular model. A USB microphone is one possible input device, not a prerequisite.
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Keep the capture stage distinct from the assistant’s conversation logic. This makes it easier to handle microphone or device errors, silence, the end of a user turn, and stream completion. For live recognition, also decide how your application will treat partial versus final results, empty recognition results, and errors. Google’s official sample demonstrates microphone streaming: Transcribe streaming audio from a microphone.
Make a transcription request for existing audio
For a stored file or otherwise available audio, Google’s Node.js quickstart shows the basic sequence: create a SpeechClient, specify the audio source and recognition configuration, call recognize, and read transcript alternatives from the response. This example follows that structure:
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const speech = require('@google-cloud/speech');
const client = new speech.SpeechClient();
async function transcribe() {
const request = {
audio: { uri: 'gs://cloud-samples-data/speech/brooklyn_bridge.raw' },
config: {
encoding: 'LINEAR16',
sampleRateHertz: 16000,
languageCode: 'en-US',
},
};
const [response] = await client.recognize(request);
const transcript = (response.results || [])
.map(result => result.alternatives?.[0]?.transcript || '')
.filter(Boolean)
.join('n');
return transcript;
}The sample’s URI points to Google-hosted audio, and its LINEAR16 encoding, 16000 Hz sample rate, and en-US language code describe that example—not universal settings. Configure the request to match your actual audio. The quickstart covers request construction and transcript extraction: Make an audio transcription request.
After extracting the transcript, send it to the assistant’s intent or dialogue layer. The recognition request does not determine what the assistant should do or produce spoken output; those require application logic and a separate audio-output path.
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Select the request mode for the interaction
Google’s overview documents three recognition modes. The right choice depends on whether the assistant is transcribing a short recording, a longer file, or a live user turn.
| Mode | When it fits | Documented constraint or use |
|---|---|---|
| Synchronous | A short audio clip when you want a response to a single request | Google states synchronous recognition processes audio of one minute or less. |
| Asynchronous, long-running | Longer audio that does not need to be handled as a live microphone conversation | Documented as a separate long-running request mode; check the current service documentation for applicable limits and behavior. |
| Streaming | Live microphone input where recognition proceeds as audio arrives | Google documents streaming recognition and provides a Node.js microphone example. |
These mode descriptions and the synchronous duration limit are from Google’s Speech-to-Text request overview. For a conversational microphone experience, streaming is the documented mode to investigate; it is not the same implementation as the one-shot file example above.
Test recognition with your actual users and environment
Before relying on transcripts to trigger assistant actions, test the complete path with the microphones, rooms, speaking styles, accents, and vocabulary you expect. Include the cases your application must handle, such as silence, an unclear phrase, a user interruption, and recognition errors. Measure recognition quality and end-to-end latency on representative audio rather than assuming a provider or request mode will perform best.
Google’s product page advertises support for “85+ languages and variants.” This is a vendor-published capability claim; it is not an accuracy guarantee for a particular language, model, speaker, or environment. Check the current service documentation for the language and model your application needs: Google Cloud Speech-to-Text.
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