book chapter
Speech artificial intelligence has become central to digital life, yet its carbon footprint is substantial. Training large-scale models requires high-performance computing, driving electricity demand and greenhouse gas emissions. While inference is lighter per task, billions of daily interactions on smartphones, smart speakers, and call centers magnify energy use. Indirect impacts include data centers, global networking, and device manufacturing, contributing embodied carbon and e-waste, worsened by always-listening devices and rapid hardware turnover. Uneven renewable energy adoption across regions deepens inequalities. To align with the Sustainable Development Goals, sustainable speech processing advances lightweight architectures, pruning, quantization, and hardware-software co-optimization. Strategies such as green data centers, edge deployment, and lifecycle assessment reflect a circular economy perspective, framing responsible AI as vital for balancing innovation, accessibility, and environmental stewardship.
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DOI: 10.4018/979-8-3373-3048-8.ch010
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