用自定义语言模型提升Vosk语音识别准确率，尤其适合专业场景。
Improving Speech Recognition Accuracy Using Custom Language Models with the Vosk Toolkit
- 基于Vosk工具包构建Python流水线，支持多格式音频预处理
- 自定义语言模型使特定领域错误率下降，对口音和噪声更鲁棒
- 开源离线方案，适合需隐私保护或实时部署的场景
尽管近年来语音识别算法发展迅速，但在多样音频格式和声学环境下实现高精度转录仍是重大挑战。本文研究如何通过在开源Vosk工具包中集成自定义语言模型，提升语音转写在不同场景下的准确性。与仅限特定音频类型的传统系统不同，该方法利用Python模块支持WAV、MP3、FLAC、OGG等多种音频格式的预处理与转换。开发了基于Python的转录流水线，对输入音频进行处理，使用Vosk的KaldiRecognizer执行语音识别，并将结果导出为DOCX文件。实验表明，自定义语言模型在涉及技术术语、方言差异或背景噪声的领域中显著降低了词错误率。本工作提供了一种成本低、离线运行的高精度转录解决方案，为自动化与实时应用开辟了新可能。
原文摘要 · Abstract (English)
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.
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