Developing Regional Languages Based AI Speech recognition system

AI-based voice assistants are a rapidly growing technology with almost limitless potential in the world of business and personal usage. They are not only improving the efficiency of processes, but also creating altogether new levels of personalization and user experience globally. When we talk of personalization, regional languages play a great role in making any person feel at ease. In a country as large as India, there is a great diversity of languages. Apart from English, which is widely spoken throughout the country, there are 22 scheduled regional languages identified by the constitution. Thus, the importance of AI Speech Recognition tools that can process these regional languages becomes much greater in the country. We all have already experienced the feel of this diversity while making interstate calls and being greeted by recorded IVR in Punjabi, Gujarati, Marathi or Tamil depending on the location of the person we are calling.

To understand how AI-driven voice assistants process a particular language, we need to take a look at the steps involved in the process. In simple words, the process requires the AI tool to capture the spoken word and turn it into text, match it with the database and then provide output which is converted into voice and offered to the user. The input is recorded in an audio format, but it is simply a wave file where the amplitude of audio changes constantly in sync with the consonants uttered. These audio files are stored by the computer and then the pre-processing is carried out where the undesirable ambient noises are filtered out. Sound is smoothened, end point detection, framing, windowing and reverberation cancelling is done to accurately identify each part of the speech. During this process, the sound samples stored in a digital format are converted into observation vectors to run the algorithms. Mihup has stored over 5000 consonants in different regional languages and ways of speaking to enable the natural language processing with great accuracy. After the running of algorithms to decode the sounds, feature extraction process is carried out wherein the magnitude of the input signal is compressed without reducing or altering the power of the speech signal received. Eventually, Recurrent Neural Networks (RNNs) are used to carry out language modelling.

What gives the edge to Mihup is not only the talent that the company has in its ranks, but its domain knowledge of the typical mix of Hindi and English or regional languages mixed with English. Further, Mihup runs deep research on accents and local dialects across regional languages. Today, the company offers tools that accurately understand English, Hindi, Bengali, Hinglish and Benglish (Bengali mixed with English), and is currently working on developing various other regional language abilities to further enhance the experience for native speakers of other languages. What makes technologies developed by Mihup even more potent for the client businesses since the waves of structured and unstructured data compiled are not only analyzed to deliver superior interaction quality, but, also to generate actionable insights for the brand. The compilation and refinement of raw data leads to a greater understanding of customer needs, service improvements and lead generation for sales and marketing teams etc. All these advancements are brought about at the backend, even when the front-end focus remains on making the voice driven interactions more accurate and result-oriented for the end users.

Non-American users find it 30% harder than the Americans to get understood by the existing western origin voice assistants. In this light, Mihup is creating technologies which will transform the world of automation and smart AI-based voice assistants in India as well as in countries around the world.