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Overview

Plaud’s transcription service accurately converts spoken language from audio into written text. It is engineered for high performance to support:
  • Developers: Build powerful applications with robust transcription capabilities.
  • Plaud Users: Get reliable, accurate records of important conversations.
  • Teams & Organizations: Create a searchable, shareable knowledge base from meetings and calls.

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Transcription Pipeline

To ensure the highest quality and readability, every audio file is processed through a sophisticated three-stage pipeline. This approach allows us to transform raw audio—often containing noise and imperfections—into polished, accurate text.
A three-stage pipeline: Preprocess, Speech-to-Text, and Post-Processing.

The three-stage pipeline for converting audio to text.

Let’s explore each stage of the process.

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Stage 1: Audio Pre-processing

The first step is to clean and prepare the raw audio signal for transcription. A clear audio input is fundamental to achieving high accuracy.
Before and after audio processing, showing the removal of background noise.

A visual comparison of an audio waveform before and after noise cancellation.

Our key enhancement features include:

Noise Reduction

Intelligently identifies and removes distracting background noise from the recording.

Echo Cancellation

Detects and eliminates echo and reverb, common in rooms with poor acoustics.

Voice Enhancement

Isolates and boosts the clarity of human speech relative to other sounds.

VAD(Voice Activity Detection)

Splits long audio into manageable chunks and filters out silence for efficient processing.

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Stage 2: Speech-to-Text (STT)

Once the audio is clean, our core STT engine converts the spoken words into a raw text transcript. This engine is optimized for a wide range of languages and specialized vocabularies.

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Stage 3: Text Post-processing

The raw text from the STT engine is then refined by a Large Language Model (LLM) to produce a final, polished document that is ready to use. This final stage includes:
  • Intelligent Punctuation: The LLM automatically adds periods, commas, question marks, and other punctuation based on the conversational context.
  • Contextual Correction: By analyzing the full conversation, the model can fix potential transcription errors that may have occurred in the previous stage.
  • Formatting: Ensures that numbers, dates, currencies, and other entities are formatted in a consistent and readable way.

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FAQ

Audio quality is the single most important factor for accuracy. While our pre-processing stage is designed to handle noise, the best results will always come from a clear recording with minimal background noise and consistent speaker volume.
Yes. Our system can detect and transcribe multiple languages within the same audio file. For the highest accuracy, we still recommend specifying the primary language if it is known.
The best way is to use the “Custom Vocabulary” feature. By providing a list of hotwords—such as brand names, technical terms, or specific jargon—you can significantly improve the recognition accuracy for those words.
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