Project Title Real-Time Speech-to-Text System for
Customer Support Automation
Skills take away From This Project Signal processing, machine learning (HMMs,
deep learning), data preprocessing,
programming (Python), real-time system
optimization, integration with APIs (Google
Speech API or CMU Sphinx),
problem-solving, business knowledge, and
collaboration.
Domain Customer Support Automation in Contact Centers
Problem Statement:
Develop a real-time speech-to-text system that can transcribe
customer-agent conversations accurately and in low-latency, enabling
automation of repetitive tasks, sentiment analysis, and actionable insights for
improving customer support.
Business Use Cases:
1. Automated Call Summarization: Generate summaries of
customer-agent interactions for faster review.
2. Sentiment Analysis: Detect customer emotions (positive, negative,
neutral) to prioritize urgent cases.
3. Keyword Extraction: Identify critical keywords (e.g., "refund," "complaint")
to categorize issues automatically.
4. Agent Performance Monitoring: Analyze agent responses for
compliance and quality assurance.
5. Chatbot Integration: Use transcribed text to feed into AI-powered
chatbots for self-service options.
6. Cost Reduction: Reduce reliance on manual transcription and improve
operational efficiency.
Approach:
Data Collection and Cleaning
● Collect audio datasets containing customer-agent conversations.
● Preprocess audio files by removing noise, normalizing volume, and
segmenting long recordings.
● Annotate datasets with corresponding transcripts for training and evaluation.
Data Analysis
Use Power BI to create dashboards showing:
● Perform exploratory data analysis (EDA) on the dataset to understand
distribution, duration, and quality of audio files.
● Analyze transcript length, vocabulary size, and language complexity.
Visualization
● Visualize audio waveforms, spectrograms, and frequency distributions to
understand signal characteristics.
Use Power BI to create dashboards showing:
● Call volume trends over time.
● Sentiment distribution across calls.
● Most frequent keywords and topics.
● Waveform and Spectrogram Plots: To visualize raw audio signals and their
frequency components.
● Call Volume Trends: Line chart showing call volume over time.
● Sentiment Distribution: Pie chart or bar graph showing the proportion of
positive, negative, and neutral calls.
● Keyword Cloud: Word cloud highlighting frequently mentioned keywords.
● Agent Performance Dashboard: Bar charts comparing agents based on
resolution time and accuracy.
Advanced Analytics
● Implement acoustic modeling using Hidden Markov Models (HMMs) or
deep learning architectures like RNNs/LSTMs.
● Train a language model using n-grams or transformer-based models
(e.g., BERT).
● Optimize the system for low-latency processing using techniques like
streaming chunking and parallel processing.
Exploratory Data Analysis (EDA)
● Audio File Statistics: Distribution of file durations, Sampling rate and
bit depth analysis.
● Transcript Analysis: Average word count per transcript, Vocabulary
size and most common words.
● Noise Levels: Measure Signal-to-Noise Ratio (SNR) across files,
● Speaker Separation:Analyze speaker turn-taking patterns in
conversations.
Power BI Integration
Use Power BI to create dashboards showing:
● Integrate the speech recognition system with Power BI to display
real-time metrics such as:
● Transcription accuracy.
● Call resolution time.
● Agent performance scores.
Results
The results should include:
● Source Code with documentation
● High transcription accuracy (>90%) for clear audio inputs.
● Low latency (<500ms) for real-time transcription.
● Accurate sentiment classification and keyword extraction.
Project Evaluation
● Transcription Accuracy: Measure Word Error Rate (WER) and Character
Error Rate (CER).
● Latency: Measure the time taken to process and transcribe audio in
real-time.
● Sentiment Analysis Accuracy: Evaluate precision, recall, and F1-score for
sentiment classification.
Data Set:
Data Set Link: Data (Dataset Name: dev-clean.tar.gz)
Data Set Explanation:
● Contains over 1,000 hours of clean speech data.
● Includes aligned transcripts for training acoustic models.
● Ideal for building robust speech recognition systems.
● A large-scale corpus of read English speech derived from audiobooks.
● Audio is sampled at 16 kHz, ensuring high-quality recordings.
● It is split into clean and noisy subsets for varied conditions.
● Subsets include 100-hour, 360-hour, and 500-hour splits for scalability.
● Transcriptions are manually curated and aligned with audio clips.
● Metadata includes speaker IDs and chapter information for additional
tasks.
● Preprocessed train-test splits facilitate easy benchmarking of ASR
models.
● Supports research in speaker verification, language modeling, and
synthesis.
● Metadata including speaker information and chapter details.
● Usage : Ideal for training and evaluating acoustic models.
Project Deliverables:
● Cleaned and labeled audio dataset with accent annotations ready for
training and evaluation.
● Includes metadata such as speaker demographics, accent type, and
phonetic features.
● A basic ASR model trained on the raw dataset to establish initial
performance metrics.
● Includes Word Error Rate (WER) and accuracy scores for different accents.
● Trained deep neural networks using CNNs for feature extraction and
RNNs/LSTMs for sequence modeling.
● Fine-tuned pre-trained models for improved performance on
multi-accent data.
● Code and documentation for applying Maximum Likelihood Linear
Regression (MLLR) or other adaptation techniques.
● Demonstrates how the model adapts to individual speakers or accent
groups.
● Scripts and tools for augmenting audio data (e.g., pitch shifting, time
stretching, noise injection).
● Simulated datasets representing underrepresented accents for balanced
training.
● Final ASR system capable of recognizing speech across diverse accents
with improved accuracy.
● Includes a user-friendly interface or API for testing.
● Detailed analysis of accuracy, WER, perplexity, and latency before and
after applying speaker adaptation and data augmentation.
● Comparison of results across different accent groups.
● Interactive visualizations showing:
● Accuracy trends across accents.
● Improvement in performance after adaptation.
● Phonetic feature distributions and error patterns.
● Insights from EDA, including accent distribution, phoneme frequency, and
noise levels.
● Visualizations highlighting challenges posed by accents and dialects.
● Comprehensive report summarizing findings, challenges, and solutions.
● Recommendations for businesses on deploying accent-aware ASR
systems.
● Complete codebase, model checkpoints, and instructions for
reproducibility.
Timeline:
The project must be completed and submitted within 10 days from the assigned
date.
