!publish!

content/factor/stillnotrobots.md

@@ -91,6 +91,12 @@ During the pandemic, this exclusion defined at least two different models of car
 
 from: Helen Hester, [Technically Female: Women, Machines, and Hyperemployment](https://salvage.zone/technically-female-women-machines-and-hyperemployment/), Salvage magazine, 2016.
 
+**Female-sounding at least**
+
+>Once you start listening you can’t stop hearing it. The voice – female, or female-sounding at least, pre-recorded ‘real’ voices or mechanised tones, or, often, a weird cut-up mixture of both, dominates the sonic landscape. From the supermarket checkout machines with their chaste motherish inquiries (‘have you swiped your Nectar card?’) to repeated assertions regarding the modes of securitised paranoia (‘in these times of heightened security’), the female voice operates as a central asset in the continued securitisation and control of contemporary space, cutting across what little is left of the public realm and providing the appearance and the illusion of efficiency, calm and reassurance in commercial environments. 
+
+- from: Nina Power, [Once You Start Listening You Can’t Stop Hearing It](https://ninapower.net/2017/12/07/once-you-start-listening-you-cant-stop-hearing-it/), *The Wire* n. 352, June 2013.
+
 **Make-up for the voice**
 
 >Accents are a constant hurdle for millions of call center workers, especially in countries like the Philippines and India, where an entire “accent neutralization” industry tries to train workers to sound more like the western customers they’re calling – often unsuccessfully. As reported in SFGate this week, Sanas hopes its technology can provide a shortcut. Using data about the sounds of different accents and how they correspond to each other, Sanas’s AI engine can transform a speaker’s accent into what passes for another one – and right now, the focus is on making non-Americans sound like white Americans.