A Comprehensive Case Study of Kothon: Transforming Data into Insights
How Kothon Analyzes Bengali Audio to Enable Data-Driven Decision-Making
It has become a prerequisite for companies to develop custom software products to stay competitive.
Introduction
In today’s data-driven environment, companies live on insights derived from consumer interactions. However, in many contact centers, important consumer insights are trapped behind massive quantities of unstructured audio recordings. Manual transcribing is slow, costly, and prone to human mistake, making it difficult to reach useful findings.
Kothon is an AI-powered automatic speech recognition (ASR) and sentiment analysis tool that addresses these issues. It enables contact centers to transcribe and analyze Bengali audio, even if the recordings are of poor quality, and gives actionable insights to improve customer experience. Kothon helps organizations make educated, data-driven choices by effortlessly integrating with customer service platforms.
Problem Statement
Many businesses struggle to understand customer sentiment, identify pain points, and extract meaningful user stories due to the inefficiencies of manual transcription and reporting. This process is not only slow but also costly, leading to missed opportunities for enhancing customer service.
To fix this, we had to:
- Extract data from call recordings.
- Accurately transcribe Bengali speech.
- Create actionable insights to guide business choices.
Additionally, traditional speech recognition models often fail to handle:
- Low-bitrate audio recordings with significant background noise.
- Multiple speakers, leading to inaccurate segmentation and attribution.
- Tokenization challenges are unique to the Bengali language, causing inefficiencies in processing.
Key Features
The analysis of Bengali audio through Kothon enables the generation of various actionable insights.
- Sentiment analysis may expose the emotional undertones of Bengali interactions, offering useful insights into consumer happiness and brand impression.
- Topic detection and categorization can identify the key themes emerging from the audio data, helping organizations understand customer interests and emerging trends.
- Keyword and phrase extraction allows for the identification of specific terms relevant to business objectives, such as product mentions or compliance-related language.
- Intent analysis can discern the purpose behind spoken Bengali, such as customer requests or complaints, facilitating more efficient handling of inquiries.
The Engineering Behind Kothon
Building Kothon required an innovative combination of speech recognition, custom tokenization, and fine-tuned machine learning models (SFT) tailored specifically for the Bengali language.
Core Model
- Base Model: OpenAI’s Whisper Medium model, fine-tuned with a custom Bengali dataset to enhance transcription accuracy.
- Tokenizer: A custom tokenizer designed specifically for Bengali, addressing the unique challenges of tokenization in the language.
- Performance Optimization: We employed LLM-based semantic processing to improve topic analysis and sentiment understanding, mitigating common transcription inaccuracies.
Dataset
To train our model effectively, we curated a high-quality dataset:
- Bengali call recordings annotated by human call center agents.
- Supervised Fine-Tuning (SFT): We experimented with multiple ASR models, including BanglaASR, before finalizing the Whisper Medium model with our custom tokenizer.
- Data Augmentation: To improve robustness, we introduced artificially generated noise to simulate real-world call center environments, improving model generalization.
Key Challenges
Low-Quality Audio
Challenge: Many call center recordings had low bitrates and significant background noise.
Solution: We fine-tuned the Whisper model with domain-specific data and implemented advanced noise reduction techniques to enhance clarity.
This included:
- Spectral subtraction for noise reduction.
- Adaptive gain control to normalize volume levels.
- Wavelet-based denoising to remove non-stationary background sounds.
Background Noise
Challenge: Noisy environments often degrade transcription accuracy.
Solution: We applied noise suppression preprocessing techniques such as
- Bandpass filtering to isolate speech frequencies.
- Deep-learning-based noise reduction using a denoising autoencoder.
Multiple Speakers
Challenge: Conversations often involve multiple speakers, making transcription and sentiment analysis complex.
Solution: We implemented speaker diarization, which includes
- Voice activity detection to segment speech vs. silence.
- Speaker embedding models to differentiate speakers.
- Clustering algorithms like spectral clustering to group speaker segments.
Token Limit Exceeding
Challenge: The Bengali language requires more tokens due to its complex structure, leading to excessive token usage.
Solution: We developed a custom tokenizer optimized for Bengali.
- Implemented subword tokenization to reduce overall token count.
- Used byte pair encoding (BPE) to improve efficiency.
- Optimized morpheme-based segmentation to preserve meaning while reducing token length.
Work-in-Progress Features
Kothon is continuously evolving, and we are actively working on new features that will further enhance customer support automation and business intelligence.
AI-Powered Chatbot
- An intelligent chatbot that uses RAG to communicate with customer data and extract insightful information.
- Utilizes reasoning models and deep research techniques to generate data-driven responses.
- Helps customer service agents find relevant information quickly, improving response time and customer satisfaction.
Automatic Ticket Generation
- Automatically detects customer issues from call recordings and generates support tickets.
- Integrates with existing CRM and ticketing systems for seamless issue resolution.
- Reduces manual work and provides speedier responses to significant customer problems.
These planned innovations will use AI-powered automation to expedite customer service operations, minimize human burden, and improve business intelligence.
Conclusion
For companies looking to extract insights from Bengali audio data, Kothon offers an essential option. Kothon turns unstructured talks into useful knowledge by overcoming the difficulties of effectively transcribing and interpreting Bengali speech from real-world, frequently low-quality recordings. Reliable sentiment analysis, topic detection, and intent recognition are made possible by its AI-powered ASR, unique tokenizer, and sophisticated noise reduction. By enabling data-driven decisions that improve customer experience, streamline operations, and propel company success in the Bengali-speaking market, Kothon enables enterprises to transcend manual procedures.
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