BackHow AI is helping firms tap users of Indian languages
Artificial Intelligence and Machine Learning are enabling machines to go beyond mere translation, with the ability to extract meanings
PremiumDo you know that close to a million bookings are done in local languages daily on the Ola app? The Ola app features 11 different regional languages with around 80% drivers using the vernacular format. Ola is just a case in point. HDFC Securities became India’s first trading house to offer stock trading services in local languages over “HDFC Sec" Android app, enabling its customers to buy and sell stocks in 12 Indian languages.
Similarly, instant messenger Hike introduced eight Indian languages in December 2015 and has localized sticker sets in 40 languages, which contribute more than 30% of the traffic on its app. Over 40% of healthcare startup Practo’s appointments and patient communication messages are in 11 Indian languages. And the government’s BHIM UPI app with over 25 million downloads is available in 13 Indian languages.
Localization of languages has indeed come of age. The internet user base in the country, which was under 100 million till a few years back, has now reached over half-a-billion—primarily led by the rapid adoption of smartphones and decreasing mobile data prices. This has propelled the Indic languages internet user base in the country as well. In 2016, close to 60% of the 409 million internet users in India were Indic language users. This number is growing exponentially. A KPMG report estimates that of the next 326 million internet users in India, 93% are expected to be local language-first users.
Global internet and technology players like Facebook, Amazon, Microsoft and Google are gearing up to cater to this demand. Facebook prompts users to post content in Indian languages while Amazon provides documentation and online support in Hindi for its Indian sellers. Google supports a number of Indian languages including those spoken by a few, such as Dhundhari, Kangri, Malvi and Nimadi on their proprietary Android keyboards. Microsoft supports e-mail addresses in 15 Indian languages across its apps and services.
Artificial Intelligence (AI) and Machine Learning (ML) technologies have only helped in furthering the cause of local languages online. The advancements in Natural Language Processing (NLP) technologies, for instance, are enabling machines to go beyond mere translation, with the ability to extract meanings, context and provide sentiment and intent analysis for text across various languages.
Google, for instance, has its Google Neural Machine Translation (GNMT) for translation between English and nine Indian languages. GNMT offers better contextual translation and human-like speaking ability to the machines. This January, Microsoft announced the integration of Artificial Intelligence (AI) and Deep Neural Networks (DNN) to improve real-time language translation for Hindi, Bengali and Tamil.
Reverie, one of the first Indian technology-based languages localization company, provides a cloud-based Language-as-a-Service (LaaS) for its enterprise customers for real-time conversions (transliteration and machine translation), search, intent and sentiment analysis, conversations across text and voice platforms Vernacular.Ai’s B2B platform helps companies build multi-lingual chatbots in Indian languages.
Similarly, Dailyhunt—a news and local language content app—uses its proprietary Machine Learning and Deep Learning algorithms to enable smart curation of content and to track user preferences to deliver real-time, personalized content and notifications. Liv.Ai, on its part, uses speech recognition technology to provide speech application programming interfaces (APIs) to enable developers to convert Speech-to-Text transcriptions using Neural Network models.
As Natural Language Processing and Understanding (NLP/NLU) technologies evolve to go beyond text to include voice and speech synthesis, and as voice-based applications become commonplace, language localization needs and supporting AI & ML technologies will also evolve to speech-to-Text, NLU engines.
We have already started witnessing the early adopters. These include e-commerce giant’s Amazon’s Alexa being prepped up for Hindi, Reverie’s Gopal being tested for seven Indian languages and Liv.AI’s engine that is being adopted by players across various industries.
Once neural network-based speech recognition engines come of age for Indian languages localization, they will find wide adoption across use cases and applications including voice-based searching from web based applications, query search on platforms of e-commerce, health, travel, media, entertainment, etc., video/audio transcription for subtitling and captioning, analysing audio for semantics and categorization, command-based embedded services and voice based assistants.
The author is founder and partner at Convergence Catalyst and also an advisor to Reverie Language Technologies Pvt. Ltd
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