How to improve voice transcription accuracy in noisy environments?

2025-08-25

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Optimization Strategies for Speech Recognition in Harsh Environments

For noisy scenes such as conference halls and factories, the following methods can be combined to improve accuracy:

Front-end noise reduction::
- Install the NoiseSuppression module (pip install noisereduce)
- Add real-time noise reduction code to audio_processor.py:reduced_noise = nr.reduce_noise(y=audio_clip, sr=16000)
Parameter tuning combinations::
- Increase VAD threshold: started_talking_threshold=0.5
- Extended speech determination: speech_pad_ms=800
- Setting the language parameter forces the language to be specified
hardware solution::
- Using a directional microphone (cardioid directivity recommended)
- Keep the device 10-15cm from the mouth
- With an external sound card (e.g. Focusrite Scarlett)
Post-processing correction::
- Integrated language model calibration (requires kenlm installation)
- Add a glossary of field terms (modify vocab.txt file)

Tests have shown that the composite solution can improve word accuracy in noisy environments from 60% to over 85%.