![]() A more diverse range of voices in the video and sound fed to algorithms will result in those algorithms’ improved ability to interpret a broader range of speech patterns. The fix involves increasing the range and diversity of the data that we feed technology. This kind of deficiency in speech recognition is relatively easy to remedy. Still, undoubtedly the increased understanding of English-speaking males is something of a “Big Five” effect: most voice recognition platforms are made by the so called Big Five (Amazon, Apple, Facebook, Google, and Microsoft), which themselves are disproportionately staffed and led by white males. For example, Judith Newman’s son Gus “speaks as if he has marbles in his mouth, but if he wants to get the right response from Siri, he must enunciate clearly.” For Newman, as a mother of a child with developmental challenges, the fact that Siri requires precise articulation has been a benefit, not a bug. On the other hand, the specificity required by voice recognition can be helpful to those trying to improve their speech clarification. The company that administered her test used a voice recognition technology trained to identify acceptable and unacceptable answers to questions although she was a highly educated native English-speaker, the algorithm deemed her answers unacceptable. One telling story is that of an Irish woman who failed an automated spoken English proficiency test while trying to immigrate to Australia. In one study testing speech recognition of different accents, English spoken with an Indian accent only had a 78 percent accuracy rate recognition of English spoken with a Scottish accent was only 53 percent accurate. If the user is a woman of color, the rate of accurately understanding her speech drops further. Testing a variety of speech activation technologies has shown that virtual assistants are more likely to understand male users than female users. Case in point: Google’s speech recognition is 13 percent more accurate for men than it is for women. Speech recognition exemplifies how partial training data has led machines to learn more about white men’s speech patterns and less about those of women and people of color. As it turns out, however, they do not always listen to everyone equally. Despite initial limitations and biases in voice technology, it is possible to create an equality machine through conscientious and deliberate social efforts.-Orly LobelĪlexa, Siri, and other voice-activated chatbots not only speak to us but listen too. Together, this indicates a larger vision. If that binary choice already seems dated, social innovation and activism is moving us a step further by developing genderless digital voices. As we see below, the trajectory is promising: More companies are letting customers pick feminine or masculine assistant voices. Relatedly, as consumers and developers, we question the automatic assignment of feminine voices to digital assistants, such as Siri and Alexa. But deliberate choices by developers can direct translation software to provide both masculine and feminine translations or glean the context to decipher a word’s correct gender. A sentence describing a politician or a business executive will default to the masculine version in the translation. Translation platforms tend to turn gendered words to their masculine iterations because the translator learns from massive amounts of historical (and some current) digital texts. Similarly, next-generation digital language translators can correct how AI translation too frequently replicates gendered biases found in human-crafted communication. Making speech technology that “sounds like me”-no matter where I live-requires public grants and collective efforts to gather voice data from many sources. ![]() ![]() Crowdsourcing voice samples from all over the world can correct the reality that most voice recognition technologies are developed exclusively for English speakers while languages spoken by smaller or poorer populations are often left behind. Social activism, consumer choice, crowdsourcing, and open-sourced projects are crucial for creating a digital platform representative of its users. ![]() These technologies are often developed in a skewed way that is not representative of the varied speech patterns, language usage, and accents of most. In this excerpt from The Equality Machine, I describe a path forward in creating more inclusive digital speech. Speech is uniquely intimate and human, yet digital personal assistants, chatbots, language translation, text-to-speech, and speech-to-text are increasingly integrated into every aspect of our lives. The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future ![]()
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