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2、There’s No Data for Male Voices This

　　This is probably the most argued point for programmers when they begin creating voice automated AI.
　　Over the years, text-to-speech systems have been predominantly trained on female voices. Because we have such rich data for female voices, companies are more likely to opt for them when creating voice automated software as it’s the most time- and cost-efficient solution.
　　Female voice recordings date back to 1878 when Emma Nutt became the first woman to be a telephone operator. Her voice was so well received, she became the standard all other companies strived to emulate. By the end of the 1880s, telephone operators were exclusively female.
　　Because of this gender switch in the industry, we now have hundreds of years of female audio recordings we can use to create new forms of voice automated AI that we know users will respond well to.
　　Why waste your time and money collecting male voice recordings and creating male-voiced AI, when you don’t know how your users will respond to it. And this leads us on to our next point...
　　The Challenges of Creating Male Voice Automation
　　As the AI industry is dominated by female voices, it should come as no surprise that creating male voice automated systems can be incredibly difficult. Let’s take a look at an example from Google.
　　In 2016, Google launched “Google Assistant” and there was a reason the tech giant went with a gender-neutral name... because Google wanted to launch its new voice assistant with both a male and female voice.
　　Unfortunately, the systems Google used to create its new “assistant” were only trained using female data, which meant they performed better with female voices.
　　Google’s older text-to-speech system would join pieces of audio together from recordings, by using a speech recognition algorithm[??lg?r???m]
　　. It worked by adding markers in different places in sentences to teach the system where certain sounds would start and end.
　　Brant Ward, the global engineering manager for text-to-speech at Google, explained that those markers weren’t as accurately placed for male voices, meaning that it would be harder to obtain the same quality for a male voice as it is for a female voice.
　　Unfortunately, the systems that Google and other companies had available to them at the time were trained on more female data than male data.
　　The team working on Google Assistant strongly advocated for both a male and female voice, but the company decided against creating a male one once it discovered how challenging it was.
　　Ward said it would have taken over a year to create a male voice for Google Assistant, and after this completion, there was no guarantee it would have been of a high enough quality or received well by users.
　　How Can We Tackle Gender Bias in Voice Automation?
　　It appears the gender bias in voice automation is down to a lack of data and widely accepted and unchallenged perceptions of the female voice. When you have all this information stacked up in front of you, creating male voice automated software may seem like an impossible task.
　　However, there are steps we can take to alter the gender bias not just in voice automation, but throughout the AI industry itself.
　　1. Invest in Machine Learning Technology
　　With new machine learning technology at our disposal, text-to-speech systems are becoming more advanced and are now more able to create naturalistic male and female voices for AI.
　　For example, Google teamed up with AI specialists DeepMind – a British AI subsidiary of Alphabet Inc. and research laboratory – with the plan to develop a new kind of text-to-speech algorithm that would reduce the number of recordings needed to create voices that closer assimilated that of a real human.
　　By 2017, both Google and DeepMind had created an algorithm named WaveNet, which helped Google develop a more naturalistic female and male voice to add to Google Assistant.
　　Today, America’s version of Google Assistant comes programmed with 11 different voices, some of which have different accents. To ensure its product is as inclusive as possible, Google assigns new users of Google Assistant with one of two basic voices – one male and one female – completely at random.