Classical codecs prove more robust to noise than neural codecs, speech enhancement significantly helps noise-affected codecs, and listening effort plus ASR-based metrics add useful nuance beyond basic intelligibility scores.
Assessing the Impact of Noise and Speech Enhancement on the Intelligibility of Speech Codecs
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abstract
Preserving speech intelligibility is a minimum requirement for speech codecs in communication. Recently, very low-bitrate neural codecs have gained interest for replacing classical codecs, reinforcing the need to evaluate whether intelligibility is preserved in realistic scenarios. In this paper, we evaluate the intelligibility and listening effort of classical and neural speech codecs in clean and noisy conditions. Further, we assess the impact of speech enhancement (SE) before coding, simulating a possible audio processing pipeline. The results show that classical codecs are more noise robust than neural codecs. Further, SE can lead to significant intelligibility and listening effort improvements for codecs otherwise negatively affected by noise. Listening effort reveals nuanced differences when intelligibility is saturated. Lastly, objective intelligibility based on automatic speech recognition is highly correlated with subjective intelligibility scores averaged per condition.
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Assessing the Impact of Noise and Speech Enhancement on the Intelligibility of Speech Codecs
Classical codecs prove more robust to noise than neural codecs, speech enhancement significantly helps noise-affected codecs, and listening effort plus ASR-based metrics add useful nuance beyond basic intelligibility scores.