When Alexa was launched in the US in 2014, there was nothing like it. The Star Trek inspired voice assistant in no time became a household name. By 2019, the number of American parents who named their child Alex dropped by two-thirds. It was unofficially a name reserved for robots.But bringing this technology to India, Amazon knew, would require a lot of backend re-engineering. There were linguistic challenges. There were challenges of fitting Alexas personality into the Indian context.Snehal Meshram, senior manager for Alexa AI-Natural Understanding, says at the core of Alexas software and hardware is a system called the spoken language understanding system (SLU). This system is what allows Alexa to understand and respond to human voices.The SLU is made of three primary components: ASR (automatic speech recognition), NLU (natural language understanding), and TTS (text to speech). The first two systems had to be built in India, says Snehal, especially since they needed people who could speak Indian languages.ASR systems use a combination of algorithms, statistical models, and machine learning techniques to analyse and transcribe spoken words and phrases. That process involves steps like audio capturing, pre-processing to remove noise, acoustic modelling. The India team had to do a lot of work to ensure that Alexa understood the differences in dialect, accents, intonations.Alexa devices in India also had to deal with a range of acoustical environments not typically seen in the US. Background noise, for instance, is a lot more in India.This meant the ASR systems had to understand how to suppress background noise, how to detect sounds more accurately, and leverage beamforming technologies to have a sharper understanding of user requests. In the end, the team had to leverage neural ASR technology to improve the system. Neural ASR technology leverages deep neural networks, allowing us to build large language models and acoustic models, and leverage that technology to then improve the systems, Snehal says. Using this, the team was able to reduce ASR errors by over 25% last year.Once Alexa has converted the speech wave into a text input using ASR tech, the voice assistant needs to understand the users intention. This is where NLU comes in. If a user says an accident has left some serious marks on a car, this is very different from a sentence like ?his marks in the exam were not that good. The word marks here represents two different contexts and two different intents. NLU models interpret the text input, and we assign intent to every slot and every entity that we hear from the user. Weve driven a lot of algorithmic improvements here as well, says Snehal.Finally, Alexa needs to respond to the user, and she must do so in a manner different from her American counterpart. In fact, this is an entity that speaks English with an Indian accent. Snehal says a lot of regional phonetics information has gone into making sure that Indian languages and accents are perfected. Alexa responds in Hindi as well as in Indian English. Here as well, we leverage a lot of our neural technology to make sure that we are continuously learning and continuously improving the accuracy, she says.Making sure Alexa has an Indian personality was another monumental task. Seema Somshekar, managing editor for Alexa India, says that just like a person, Alexa has been programmed to have likes, opinions, preferences, a sense of humour, and even the ability to sing songs, even if it means its not pitch perfect. Indian contextual experience is what our customers will be looking for, she says.To enable that, Seemas team had to help Alexa learn, understand and be aware of things that Indians love. We needed to keep in mind the diversity of the country, the way in which people speak, the cultural nuances of the geography, she says.
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Deep neural networks used to perfect Alexas Indian avatar - Times of India
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