Project Title Accent-Aware Speech Recognition System
Using Deep Learning and Speaker Adaptation
Techniques
Skills take away From This Project Deep learning (CNNs, RNNs), speaker
adaptation techniques (MLLR), data
augmentation, signal processing, speech
recognition, Python programming, data
preprocessing, model fine-tuning,
visualization (Power BI), problem-solving, and
domain-specific knowledge in linguistics and
accents.
Domain Automatic Speech Recognition (ASR) systems,
virtual assistants, transcription services, and
language learning platforms
Problem Statement:
Speech recognition systems often struggle to generalize across diverse
speakers with varying accents and dialects. This issue leads to reduced
accuracy and usability, especially in multi-accent environments.
The goal of this project is to develop an accent-aware ASR system that
leverages deep learning models (CNNs and RNNs), speaker adaptation
techniques (e.g., MLLR), and data augmentation strategies to improve
recognition accuracy across different accents and dialects.
Business Use Cases:
1. Transcription Services
a. Automating transcription for podcasts, interviews, and meetings.
2. Accessibility Tools
a. Providing real-time captions for videos or live events for people
with hearing impairments.
3. Customer Support Automation
a. Enhancing voice bots to understand and respond accurately to
user queries.
4. Virtual Assistants
a. Improving the accuracy of voice commands in smart devices like
Alexa or Google Assistant.
5. Language Learning Platforms
a. Offering feedback on pronunciation and grammar for non-native
speakers.
Approach:
Data Collection and Cleaning
● Collect a large-scale dataset containing speech samples from speakers with
diverse accents and dialects.
● Preprocess audio data: normalize volume levels, remove background noise,
and segment audio into smaller chunks.
● Label data with corresponding transcriptions for supervised learning.
Data Analysis
Use Power BI to create dashboards showing:
● Accuracy metrics across different accents.
● Improvement in accuracy after applying speaker adaptation techniques.
● Phonetic feature distributions for each accent group.
Visualization
● Accuracy Heatmap: Visualize recognition accuracy across different
accents before and after applying speaker adaptation.
● Confusion Matrix: Show misclassifications for phonemes or words specific
to certain accents.
● Performance Trends: Plot accuracy improvement over epochs during
training.
● Feature Importance: Highlight phonetic features contributing most to
accent differentiation.
Advanced Analytics
● Train deep learning models (CNNs for feature extraction, RNNs/LSTMs
for sequence modeling).
● Fine-tune pre-trained models using transfer learning.
● Implement Maximum Likelihood Linear Regression (MLLR) for speaker
adaptation.
● Use data augmentation techniques (pitch shifting, time stretching,
noise injection) to simulate diverse accents.
Exploratory Data Analysis (EDA)
● Audio Length Distribution: Analyze the duration of audio clips to
identify outliers.
● Accent Distribution: Visualize the proportion of samples per accent
group.
● Phoneme Frequency: Explore phoneme usage patterns across
accents.
● Noise Levels: Examine the presence of background noise in
recordings.
● Baseline Model Performance: Evaluate the performance of a basic ASR
model on the raw dataset.
Power BI Integration
Use Power BI to create dashboards showing:
● Accuracy metrics of different models.
● Feature distributions and correlations
Results
The results should include:
● Improved recognition accuracy for underrepresented accents due to data
augmentation and speaker adaptation.
● A robust ASR system capable of handling diverse accents with minimal
degradation in performance.
Project Evaluation
● Word Error Rate (WER): Measure the percentage of incorrectly
recognized words. Target: Reduce WER by at least 20% for
underrepresented accents.
● Perplexity: Evaluate the quality of language modeling.
● Accuracy by Accent Group: Compare recognition accuracy
across different accents.
● Improvement After Adaptation: Quantify the gain in accuracy
after applying MLLR or other adaptation techniques.
● Latency: Ensure real-time processing capabilities for practical
applications.
● User feedback scores for perceived accuracy and usability.
● Computational efficiency (training time, inference speed).
Data Set:
Data Set Link: Data (Dataset Name: Common Voice Delta Segment 21.0)
Data Set Explanation:
● Audio Recordings: The dataset contains short audio clips (typically 5-10
seconds) of people reading sentences aloud, captured in various
environments.
● Text Transcriptions: Each audio clip is paired with a corresponding text
transcription, ensuring alignment between spoken words and written text.
● Multilingual Content: The dataset includes recordings in over 100
languages, making it suitable for training multilingual speech recognition
models.
● Metadata Availability: Metadata such as speaker age, gender, accent, and
language proficiency is provided, enabling detailed analysis and
customization of models.
● Crowdsourced Diversity: Contributions come from volunteers worldwide,
resulting in diverse accents, dialects, and speaking styles.
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.
