Project Title Building a Speech-to-Text System with

Integrated Language Modeling for Improved

Accuracy in Transcription Services

Skills take away From This Project Signal processing, machine learning (HMMs,
deep learning), data preprocessing,
programming (Python), visualization (Power
BI), natural language processing (NLP),
problem-solving, business knowledge, and
collaboration.

Domain Healthcare, Customer Service, Accessibility

Tools, IoT and Smart Devices, Security and
Surveillance, Education and E-Learning,
Entertainment and Media, Automotive,

Problem Statement:

Traditional speech recognition systems often struggle with accurately

transcribing spoken language due to variations in accents, background noise,
and contextual ambiguity. Additionally, standalone acoustic models may fail to
capture the linguistic patterns of the target language, leading to suboptimal
performance.

This project aims to address these challenges by integrating a robust

n-gram-based language model with an acoustic model to improve transcription
accuracy and contextual understanding.

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 text corpus (e.g., Wikipedia articles, books, or

transcripts) for training the language model.
●​ Gather audio datasets (e.g., LibriSpeech, Common Voice) for training
the acoustic model.
●​ Clean the data by removing noise, normalizing text, and aligning audio
with transcripts.

Data Analysis

●​ Perform tokenization and frequency analysis on the text corpus to

identify common n-grams.
●​ Analyze the audio dataset to extract features such as MFCCs
(Mel-Frequency Cepstral Coefficients).

Visualization

●​ Visualize n-gram frequencies using bar charts or word clouds.

●​ Plot confusion matrices to evaluate transcription accuracy.
●​ Use Power BI to create dashboards showing transcription
performance metrics (e.g., Word Error Rate).
●​ Word Cloud : Display the most frequent n-grams in the text corpus.
●​ Confusion Matrix : Show errors in transcription (e.g., insertions, deletions,
substitutions).
●​ Performance Metrics Dashboard : Use Power BI to visualize metrics like Word
Error Rate (WER), accuracy, and precision.
●​ Audio Feature Visualization : Plot MFCCs or spectrograms to analyze audio
characteristics.

Advanced Analytics

●​ Train an n-gram language model using the text corpus.

●​ Train an acoustic model using a machine learning algorithm (e.g., HMM
or deep learning).
●​ Integrate the language model with the acoustic model to improve
transcription accuracy.

Exploratory Data Analysis (EDA)

●​ Analyze the distribution of word lengths and sentence lengths in the

text corpus.
●​ Identify the most common unigrams, bigrams, and trigrams.
●​ Explore the correlation between audio features (e.g., pitch, energy)
and transcription accuracy.
●​ Compare the performance of different acoustic models (e.g., HMM vs.
deep learning).
 Power BI Integration

Use Power BI to create dashboards showing:

●​ Accuracy metrics of different models.

●​ Feature distributions and correlations

Exploratory Data Analysis (EDA)

●​ Analyze the distribution of audio durations and sampling rates.
●​ Identify common types of noise in the dataset.
●​ Explore the correlation between extracted features (e.g., MFCCs and
pitch).
●​ Evaluate the effectiveness of VAD in isolating speech segments.
●​ Compare the performance of different noise reduction techniques.

Results

The results should include:

●​ The integrated system should achieve higher transcription accuracy
compared to a standalone acoustic model.
●​ The n-gram language model should reduce errors caused by contextual
ambiguity.
●​ Visualizations should clearly demonstrate improvements in performance
metrics.
Recommendation to End User

●​ Businesses should adopt integrated systems combining language models with

acoustic models for better transcription accuracy.
●​ Continuous improvement can be achieved by fine-tuning the models with
domain-specific data (e.g., medical, legal, or technical vocabulary).

Project Evaluation

●​ Word Error Rate (WER) : Measures the percentage of words

incorrectly transcribed.
●​ Accuracy : Percentage of correctly transcribed words.
●​ Precision, Recall, and F1-Score : Evaluate the model's ability to
handle specific types of errors.
●​ Training Time : Measure the computational efficiency of the
model.
●​ User Feedback : Conduct surveys to assess user satisfaction
with the transcription quality.

Data Set:
Data Set Link: Data
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:

●​ Detailed explanation of the methodology, including data preprocessing,

model training, and integration steps.
●​ Algorithms used (e.g., n-gram models, HMMs, deep learning).
●​ Instructions for deploying and using the speech-to-text system.
●​ Guidelines for fine-tuning the system for domain-specific applications.
●​ Source Code : Python scripts for data preprocessing, model training, and
evaluation. Integration code for combining the acoustic model and
language model.
●​ Jupyter Notebooks for exploratory data analysis (EDA) and visualization.
●​ Step-by-step implementation of the n-gram language model and acoustic
model.
●​ Models: Trained Language Model : N-gram model trained on the text
corpus. Saved as a serialized file (e.g., .pkl or .json) for reuse.
●​ Trained Acoustic Model : Acoustic model trained on the audio dataset.
●​ Exported in a format compatible with deployment (e.g., TensorFlow
SavedModel, PyTorch .pt).
●​ Integrated Speech-to-Text System : A unified pipeline combining the
language model and acoustic model for transcription tasks.
●​ Visualizations: Static Visualizations : Word clouds, bar charts, and
confusion matrices saved as images or PDFs.
●​ Interactive Dashboards : Power BI dashboard showcasing performance
metrics (e.g., WER, accuracy, precision). Interactive filters to analyze
performance across different datasets or user groups.
●​ Evaluation Metrics Performance Metrics Report : Word Error Rate (WER),
accuracy, precision, recall, and F1-score for the transcription system.
●​ Comparison of standalone acoustic model vs. integrated system.
●​ Benchmarking Results : Comparison of results with baseline models (e.g.,
traditional HMM vs. deep learning). Insights into the impact of n-gram size
(unigram, bigram, trigram) on performance.
●​ Prototype Application (Optional) Speech-to-Text Demo : A lightweight
application or web interface where users can upload audio files and
receive transcriptions. Built using frameworks like Flask, FastAPI, or
Streamlit.

Timeline:

The project must be completed and submitted within 10 days from the assigned
date.