Noise cancellation with speech-to-text: The pros and cons

Noise cancellation in speech-to-text (STT) systems presents both advantages and challenges, according to recent insights from Applied AI Engineer David Lange. While noise cancellation can enhance conversational flow in voice agents by reducing false "speech started" events and improving turn-taking, it may inadvertently degrade transcription accuracy. This paradox arises because modern automatic speech recognition (ASR) models are already trained on datasets that include noisy environments, and preprocessing with noise cancellation can duplicate efforts, often with diminished context and precision. The decision to implement noise cancellation should be context-specific, taking into account whether persistent background noise is an issue and considering alternatives like Voice Activity Detection (VAD) tuning, which can be more effective for intermittent sounds and does not negatively impact STT accuracy. Moreover, noise cancellation introduces additional latency and costs that need to be justified by tangible improvements in real-world applications. Lange suggests using noise cancellation judiciously, primarily directing cleaned audio to VAD processes while preserving the original audio for the STT model to maintain transcription integrity.

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