Improving Speech Recognition Accuracy Using Custom Language Models with the Vosk Toolkit
It provides a cost-effective, offline solution for high-accuracy speech recognition, addressing problems for users needing transcription in varied settings, though it is incremental as it builds on existing open-source tools.
This work tackled the challenge of achieving high transcription accuracy across diverse audio formats and acoustic environments by incorporating custom language models with the Vosk Toolkit, resulting in reduced word error rates, especially in domain-specific scenarios involving technical terminology, varied accents, or background noise.
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.