Auto Punction in Speech-to-Text Indian Lamguages

Auto-Punctuation: Redefining Speech-to-Text for Indian Languages

Written by Flashscribe

Punctuations can make or break the meaning of a sentence. Writing with the right punctuations can guide the pace of reading while clarifying sentence complexity and variety to the reader. Today, they’re an inevitable part of most of the written languages across the world. However, not only Greek and Roman but also most of the Indian languages like Tamil and Telugu were originally written withoutspacingorpunctuations about several centuries ago.

Most of the common punctuations that we use today in Indian languages such as Full-stop (.), Comma (,), Question mark (?), Exclamation mark (!), Semi-colon (;) and Colon (:) were borrowed from English during the time of British rule in India. They have grammatical functions and add contextual meaning to the written language for better understanding.  

In the digital era, computers apply Artificial Intelligence (AI) to understand human languages. The speech engines in the speech to text apps like Flashscribe leverage Natural Language Processing (NLP) and Natural Language Understanding (NLU) to convert speech to text with grammatical and contextual accuracy. Though AI-driven transcription apps are yet to achieve the accuracy rate of human transcriptionists at present,  they provide advanced speech analytics features like intent identification and emotion analysis for better customer experience across diverse industries. 

Intent Identification 

Speech-to-Text Intent Identification

Flashscribe’s speech recognition engine can identify the intent of customers from voice calls using NLP techniques like Text Categorization and Syntactic Parsing. It later classifies the texts based on their intentions into pre-defined categories such as Enquiry, Demo Request, Purchase, and Feedback. Understanding customer intents from voice-based customer interactions can help gather actionable insights and automate lead generation for businesses. 

क्या आपके उत्पाद की वारंटी है? – Does your product have a warranty? (Enquiry)

मैं एक उत्पाद डेमो के लिए साइन अप करना चाहूंगा. – I would like to sign up for a product demo. (Interest)

मेरा खाता क्यों अवरुद्ध है? – Why is my account blocked? (Grievance/Feedback)

Our speech engine senses and converts pause and voice inflections into appropriate punctuation marks for both recorded audio and live speech. The auto-punctuation feature for Indian languages helps make sense of non-verbal communication such as pauses and change in the pitch of the voice in customer calls while adding context to intent identification from utterances. 

Emotion Analysis 

Speech-to-Text Emotion Analysis

The AI-driven speech-to-text app, Flashscribe for Indian languages goes a step further from intent identification and includes emotion analysis for live speech and recorded audio on the app. Generally, emoticons create emojis onscreen as the visual indicators of varied emotions in the digital world. It employs NLU techniques such as Semantic Parsing and Sentiment Analysis to assign specific emojis to text in transcripts based on five different emotional classes (neutral state, surprise, happiness, sadness, and anger).  

मेरा खाता क्यों अवरुद्ध है? – Why is my account blocked? (Surprise)

मुझे क्षमा करे. – I’m sorry. (Sadness)

The auto-punctuation feature supports emotion analysis since every punctuation mark is related to specific emotions. For instance, the words spoken with falling intonation have a full stop at the end of the sentence which could relate to neutral state or sadness while words spoken with a rising intonation have a question mark at the end of the sentence that could relate to surprise or anger. 

It’s a gargantuan job for businesses to listen, understand, and respond appropriately to their customers within short turnaround time. Flashscribe’s auto-punctuation feature is a major milestone in the advancement of speech engines for regional languages. As a result, AI-driven transcription apps for Indian languages can help large enterprises as well as small and medium-sized businesses to improve their customer experience with advanced speech analytics features like intent identification and emotion analysis. Wouldn’t it be helpful to have a speech-to-text app that can understand your customers and do the groundwork for your business? 

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