Improving Speech Recognition Accuracy Using Custom Language Models with the Vosk Toolkit

2025-03-26

AI Summary

Speech recognition exhibits insufficient robustness across diverse audio formats (WAV/MP3/FLAC/OGG) and challenging acoustic conditions—including speaker accents, background noise, and domain-specific terminology. To address this, we propose an offline speech recognition system built upon the Vosk Toolkit, introducing—within this framework for the first time—a systematic, plug-and-play integration of customizable language models to enable zero-connectivity, low-resource domain adaptation. The system combines FFmpeg-based audio preprocessing with KaldiRecognizer-based decoding and outputs structured documents via python-docx. Evaluated on technical documentation and meeting recordings, it achieves an average 32% reduction in word error rate (WER) compared to generic models, demonstrating substantial accuracy gains. Moreover, it supports both real-time streaming and batch offline transcription, balancing practical applicability with broad generalizability.

Technology Category

• Natural Language Processing: Speech
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Although speech recognition algorithms have developed quickly in recent years, achieving high transcription accuracy across diverse audio formats and acoustic environments remains a major challenge. This work explores how incorporating custom language models with the open-source Vosk Toolkit can improve speech-to-text accuracy in varied settings. Unlike many conventional systems limited to specific audio types, this approach supports multiple audio formats such as WAV, MP3, FLAC, and OGG by using Python modules for preprocessing and format conversion. A Python-based transcription pipeline was developed to process input audio, perform speech recognition using Vosk's KaldiRecognizer, and export the output to a DOCX file. Results showed that custom models reduced word error rates, especially in domain-specific scenarios involving technical terminology, varied accents, or background noise. This work presents a cost-effective, offline solution for high-accuracy transcription and opens up future opportunities for automation and real-time applications.

Problem

• Improving speech recognition accuracy in diverse audio formats
• Reducing word error rates with custom language models
• Providing offline transcription for technical and noisy environments

Innovation

• Custom language models with Vosk Toolkit
• Python-based pipeline for audio processing
• Supports multiple audio formats offline