And speech is a dynamic process without clearly distinguished parts. The software has to cope with varied speech patterns, and individuals’ accents.

The key challenge for developing speech recognition software, whether it’s used in a computer or another device, is that human speech is extremely complex. Some of the in-car applications include navigation, asking for weather forecasts, finding out the traffic situation ahead, and controlling elements of the car, such as the sunroof, windows, and music player. In-car applications have lots of mileage (excuse the pun). Speech recognition is also used in smart watches, household appliances, and in-car assistants. And the popularity of speech to control devices is testament to dedicated products that have dropped in large quantities such as Amazon Echo, Google Home, and Apple HomePod. The assistants use voice queries and a natural language user interface to attempt to answer questions, make recommendations, and perform actions without the requirement of keyboard input. Witness the rise of intelligent personal assistants, such as Siri for Apple, Cortana for Microsoft, and Mycroft for Linux. The speech recognition market is estimated to be worth about $10 billion a year in the next four years. And, according to a study by Stanford University, the University of Washington and Chinese search giant Baidu, smartphone speech is three times quicker than typing a search query into a screen interface. The better the accuracy, the more likely customers will engage with this method of control.

But technological advances have meant speech recognition engines offer better accuracy in understanding speech. Speech is probabilistic, and speech engines are never 100% accurate. Speech is an increasingly popular method of interacting with electronic devices such as computers, phones, tablets, and televisions.

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