International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 12 | Dec 2025
p-ISSN: 2395-0072
www.irjet.net
Conversational Analytics System Kilambi Pavanadithya1, Malliboyini Venkat Kalyan2, Yatakalla Sarika3 and Gondra Jahnavi4 ,Mr. Jagadeeshwar Reddy V5 1, 2, 3, 4 Department of CSE(Data Science), AVN Institute of Engineering and Technology under the guidance of 5 Asst. Professor, Department of CSE(Data Science), AVN Institute of Engineering and Technology
-----------------------------------------------------------------------***-------------------------------------------------------------------------------
Abstract-With the rapid rise in spoken interactions happening in digital spaces, organizations, and research settings, there's a
growing demand for reliable and automated conversation analytics. The goal of conversation analytics is to turn unstructured speech data into meaningful, structured insights, which helps us gain a better understanding of dialogues involving multiple speakers and the content being discussed. Thanks to recent advances in speech and language technologies, we're now able to analyze conversational audio more accurately and with better context than ever before. This paper focuses on how conversation analytics can transform raw speech into valuable analytical information using techniques like automatic speech recognition, speaker diarization, and natural language processing. These methods help identify who’s speaking, transcribe what they’re saying, and pull out linguistic and contextual patterns from conversations. The insights we get from analyzing this conversational data enhance our understanding of communication dynamics and support better decisionmaking in various fields. Overall, this study underscores the increasing academic and practical importance of automated conversation analysis, especially when it comes to managing large volumes of speech data effectively. Index Terms—Automatic Speech Recognition, Conversation Analytics, Natural Language Processing, Speaker Diarization, Speech Processing
I. INTRODUCTION One of the easiest ways we connect with each other is through talking. Conversations are super important in settings like meetings, customer service, education, healthcare, and media. With more people chatting on digital platforms, tons of conversational audio is generated every day. But going through all that audio by hand is tough and takes a lot of time, especially with long discussions featuring different speakers. Because of this, automated conversation analytics has become a key area of research. Conversation analytics is all about pulling valuable insights from spoken interactions using techniques like speech processing and natural language processing. Unlike traditional audio analysis, which mainly focuses on the signals, conversation analytics digs into the flow of ideas, who’s talking, and the sequence of what’s being said. Some of the main parts of conversation analytics include automatic speech recognition, figuring out who's speaking, and linguistic analysis. By blending these elements together, we can create a structured model of conversational data. Lately, breakthroughs in deep learning have really boosted how well speech recognition and speaker identification systems work. Now, we can understand real-life conversations even when people talk over each other, when there's background noise, or when speakers have different accents. Plus, advances in natural language processing have allowed us to dive deeper into the spoken words—for example, spotting sentiment, figuring out intentions, and pinpointing context. These upgrades have made automated conversation analysis much more reliable and scalable, which is why it's gaining popularity in both research and practical use. But conversation analytics is more than just turning speech into text. It gives us insights into communication styles, personality traits of speakers, and overall interaction patterns. These insights are really helpful for figuring out how effective conversations are, understanding group discussions, and helping with decision-making. As the need for smart speech analysis tools increases, conversation analytics is becoming an essential field that transforms raw audio into meaningful insights.
II. LITERATURE SURVEY Over the past two decades, there's been a lot of exciting progress in the field of speech and language processing, paving the way for the conversation analytics systems we have today. Early contributors like Jurafsky and Martin[1] really set the
© 2025, IRJET
|
Impact Factor value: 8.315
|
ISO 9001:2008 Certified Journal
|
Page 976