Abstract

The AI Call Assistant project focuses on developing an automated system that processes call recordings and generates concise summaries using extractive summarization techniques. This system is designed to extract key information, such as action items, critical discussions, and customer feedback, from call recordings, enhancing productivity and saving time for businesses. The use of natural language processing (NLP) ensures high-quality summaries that retain the essence of the conversation.

Existing System

Traditional methods of summarizing call recordings involve manual transcription and summarization, which are time-consuming, prone to errors, and require substantial human resources. Current automated systems often fail to accurately identify the context or key highlights due to limited understanding of conversational nuances.

Proposed System

The proposed AI call assistant employs extractive summarization techniques that identify and extract the most relevant segments of the call. Key features include:

• Speech-to-Text Conversion: Converts audio recordings into text using advanced speech recognition models.
• Contextual Understanding: Uses NLP algorithms to analyze the conversational flow and detect important topics and intents.
• Summarization: Generates a concise extractive summary highlighting critical points such as action items, customer concerns, and resolutions.
• Multi-Language Support: Capable of handling recordings in multiple languages and dialects.
• Integration: Easily integrates with customer relationship management (CRM) and other enterprise systems.

Methodology

1. Speech Recognition: Audio recordings are transcribed using pre-trained speech-to-text models.
2. Text Preprocessing:
   • Tokenization
   • Removal of stopwords
   • Lemmatization
3. Keyword Extraction: Identifies keywords and key phrases using techniques such as TF-IDF and RAKE (Rapid Automatic Keyword Extraction).
4. Sentence Scoring: Scores sentences based on relevance and importance using models like BERT or GPT embeddings.
5. Extractive Summarization: Selects high-scoring sentences to create the summary.
6. Post-Processing: Ensures grammatical correctness and coherence in the generated summary.

Technologies Used

• Speech Recognition: Google Speech-to-Text API, Whisper, or DeepSpeech.
• Natural Language Processing: SpaCy, NLTK, Hugging Face Transformers.
• Machine Learning Models: BERT, GPT, or similar transformer-based models.
• Backend: Python, TensorFlow, PyTorch.
• Frontend: Web-based interface using frameworks like React or Angular.

Applications

• Customer Support: Summarizes customer queries and responses for improved service.
• Sales Calls: Highlights key points from sales conversations.
• Meeting Recordings: Captures action items and decisions for internal team meetings.
• Compliance Monitoring: Ensures adherence to regulatory guidelines by summarizing sensitive conversations.

Advantages

• Saves time and effort by automating call summarization.
• Improves accuracy and reliability of summaries.
• Reduces operational costs by minimizing manual intervention.
• Enhances customer satisfaction by quickly identifying and addressing concerns.