Embracing regional language tech for wider reach
NLP helps in understanding and generating human language through algorithms and computational linguistics.
BENGALURU: Imagine an artificial intelligence (AI) chatbot, such as Google Bard or ChatGPT by OpenAI, answering all the queries posed to it in local languages. It means the AI tool is able to understand questions or suggestions—in both voice and text—in a regional language and produces results in the same language. Not only will it help companies reach a wider audience and user base, it will also benefit the large pool of non-speaking English population.
From Zomato to Amazon to microblogging site Koo, a handful of customer-centric companies have already started providing their offerings in regional languages.
To make this happen, companies rely on translation tools as well as different language-based technologies such as natural language processing (NLP), speech recognition, machine translation and sentiment analysis (also known as data mining, which is a technique to know if the data is positive, negative or neutral).
NLP involves the interaction between computers and human language, explains Biplab Chakraborty, co-founder and chief operating officer at Mihup.ai, a conversational AI platform that specialises in speech recognition and natural language processing solutions.
NLP helps in understanding and generating human language through algorithms and computational linguistics. This technique is used to process and analyse user-generated content in local languages, such as customer reviews, user queries, and social media posts.
Speech recognition, as the name indicates, enables the conversion of spoken language into written text. This technology is used in voice assistants and voice-enabled applications to provide support in local languages.
Machine translation, on the other hand, plays a crucial role in converting content from one language to another. Companies employ machine translation systems to enable users to interact in their preferred languages.
Sentiment analysis gives a proper context to the content being captured. It employs NLP techniques to determine the opinion expressed by voice or text. This technology is utilised to analyse user feedback, reviews, and social media conversations in local languages. It helps companies understand users’ sentiments and tweak their strategy or make appropriate changes in their products and services to match users’ expectations.
With automated services in regional languages gaining traction, tech solutions such as ChatGPT are in great demand now. To reach the large, untapped market of non-English speaking people, YouTubers and influencers are now looking for AI solutions to deliver Hindi or Hinglish scripts from original English scripts, as they do not want to spend huge money on content creators or translators.
LARGE LANGUAGE MODELS (LLM)
Experts say that LLM uses deep learning algorithms that can perform NLP tasks. They say there is a need to create LLMs for regional languages. At present, there is an enormous amount of data available in English compared to local languages and companies need to find ways to have huge data in local languages
VERNACULAR VOICE RECOGNITION
This technology enables the recognition and understanding of spoken language in local or regional languages and converts it into text. The technology understands any dialectal variations, non-standard pronunciations and language-specific nuances. It also decodes mixed language
In this, a machine transcribes audio or video without manual intervention. The auto-generated subtitles that we see on YouTube videos or Instagram reels these days are an example of this technology. It has many important applications in data analysis and intelligence building