Research shows gender bias in Google's voice recognition

Voice recognition technology promises to make our lives easier, letting us control everything from our phones to cars to home appliances. Just talk to our tech, and it works.

As the tech becomes more advanced, there’s another issue that’s not as obvious as a failure to process simple requests: Voice recognition technology doesn’t recognize women’s voices as well as men’s. 

According to research from Rachael Tatman, linguist researcher and National Science Foundation Graduate Research Fellow at the University of Washington, Google’s speech recognition software has gender bias. 

She realized significant differences in the way Google’s speech recognition software auto-captions video on YouTube. It was much more consistent on male voices than female. She said the results were “deeply disturbing.” 

Tatman said she hand-checked more than 1,500 words from annotations across 50 different videos and discovered a glaring bias. 

paper published last year that describes how Google built a speech recognizer specifically for children. In the paper, the company notes that speech recognition performed better for females. Additionally, Google said it trains voice recognition across gender, ages, and accents.

Liao et al. 2015), we found that our speech recognizer performed better for females, with 10% lower Word Error Rate. (In that paper we actually measured 20% higher Word Error Rate for kids—which we’ve been working hard to improve since then.)  

Speech recognition has struggled to recognize female voices for years. Tatman cites a number of studies in her post, including voice tech working better for men in the medical field, and that it even performs better for young boys than girls. 

When the auto industry began implementing voice commands in more vehicles, women drivers struggled to tell their cars what to do, while their male counterparts had fewer problems getting vehicles to operate properly. 

It’s a similar problem to biases in algorithms that control what we see on the web—women get served ads for lower-paying jobs, criminal records turn up higher in searches for names commonly associated with someone of African-American descent, and the software some law enforcement agencies use is biased against people of color. And this isn’t the first time Google’s automated systems have failed; last year, the company came under fire when its image recognition technology labeled an African-American woman a “gorilla.” 

Tatman said the best first step to address issues in voice tech bias would be to build training sets that are stratified. Equal numbers of genders, different races, socioeconomic statuses, and dialects should be included, she said.

Automated technology is developed by humans, so our human biases can seep into the software and tools we are creating to supposedly to make lives easier. But when systems fail to account for human bias, the results can be unfair and potentially harmful to groups underrepresented in the field in which these systems are built.