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Build a Real-time Speech Recognition Sentiment Analysis Tool
A guide to building a sentiment analysis tool with Streamlit and NLP for customer call centers where audio is automatically translated to text and processed to get the sentiment of the caller.
According to TechTarget “Speech recognition, or speech-to-text is the ability of a machine or program to identify words spoken aloud and convert them into readable text.” Speech recognition technologies have made tremendous strides in recent years, becoming an integral part of corporate and individual lifestyles, standing at the forefront of disruptive innovations.
In big industries like tech, healthcare, finance, customer service, etc, spoken words or audio are generated daily: in meetings, through calls, through presentations, and audio notes. But the words and audio alone mean nothing if they cannot be adequately processed for analysis.
Why is Natural Language Processing Important in Speech Recognition?
In industries where voluminous speech data is the norm, harnessing spoken words for analysis unlocks a vast amount of untapped potential for uncovering nuanced patterns, sentiments, and critical insights buried within spoken interactions. This is where Natural Language Processing (NLP) plays a critical role.
Consider the finance sector, where rapid decision-making is paramount. Speech recognition not only quickens the extraction of critical information from financial reports or market updates, but also enables real-time analysis of earning calls, and investor discussions. The nuances in tone and emphasis detected by sophisticated algorithms contribute to a better understanding of market sentiment, a game-changer in an industry where split-second decisions can yield substantial outcomes.
Similarly, in customer service, the integration of speech recognition and text analysis serves as a backbone for deciphering customer feedback across various channels. Beyond conventional surveys and written reviews, speech recognition technology empowers industries to glean valuable insights from customer calls, social media interactions, and support chats. This creates a more holistic understanding of customer sentiment, preferences, and pain points.
