How I Improved Speech-to-Text Accuracy

2026-04-09 · Source: AI Advances - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

A two-pass, LLM-based post-processing method significantly improves speech-to-text (STT) transcription quality, particularly for resource-constrained languages. This technique addresses common STT errors, including spelling mistakes, inconsistent capitalization, incorrect hyphenation, and missing function words. The first pass focuses on repairing spelling and ensuring consistency, while the second pass tackles context-related issues like compound words and grammatical completeness. This approach has demonstrated a reduction in Word Error Rate (WER) across various STT models and can be adapted for different languages by modifying the LLM prompts. The method involves building a TranscriptEnhancer component to orchestrate these two passes.

Key takeaway

For NLP Engineers working with speech-to-text systems, especially in low-resource languages, consider implementing a two-pass LLM post-processing pipeline. This method can significantly reduce Word Error Rate by systematically correcting spelling, consistency, and contextual errors. You should experiment with prompt engineering to adapt the system to specific language nuances and evaluate its performance against your current STT models.

Key insights

A two-pass LLM post-processing method effectively corrects common speech-to-text transcription errors.

Principles

• Contextual repair requires multiple passes.
• LLMs can correct STT errors.
• Prompt engineering enables language adaptation.

Method

The method uses a two-pass LLM post-processor: Pass 1 corrects spelling and consistency, and Pass 2 repairs context-related issues like compound words and missing function words.

In practice

• Implement a TranscriptEnhancer component.
• Adapt prompts for new languages.
• Evaluate WER reduction on STT models.

Topics

• Speech-to-Text Accuracy
• LLM Post-processing
• Word Error Rate
• Transcription Issues
• Context-Based Repair

Best for: NLP Engineer, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Advances - Medium.