Transcript Analyzer (ASR Evaluation) Description Transcript Analyzer is a Python-based tool for evaluating Automatic Speech Recognition (ASR) model outputs by calculating Word Error Rate (WER) against a reference transcript. The tool supports comparison of multiple transcription outputs to benchmark model performance. This project was developed as part of an academic research and coursework project focused on speech recognition and natural language processing evaluation.
Technologies Used Python Natural Language Processing (NLP) jiwer (WER evaluation library)
Project Features Loads and processes reference and hypothesis transcripts Computes Word Error Rate (WER) for multiple ASR outputs Ranks transcription models based on accuracy Simple, script-based evaluation workflow
Learning Objectives ASR evaluation metrics (WER) NLP text preprocessing and comparison Experimental benchmarking of ML models Research-oriented scripting and result analysis
Future Improvements
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Advanced ASR Metrics Add Character Error Rate (CER) for languages with complex morphology Support Sentence Error Rate (SER) for full-utterance evaluation Why it matters: Shows deeper understanding of speech recognition evaluation beyond WER.
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Text Normalization Pipeline Normalize text before evaluation (lowercasing, punctuation removal, number expansion) Handle fillers (uh, um) and hesitations Why it matters: This is exactly what real ASR research pipelines do.
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Model-wise Result Export Export evaluation results to CSV or JSON Include model name, WER score, and ranking Why it matters: Makes results reproducible and research-ready.
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Visualization of Results Plot WER comparison using matplotlib / seaborn Bar charts comparing ASR models Why it matters: Recruiters love seeing “analysis + visualization”.
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Dataset Scaling Support multiple reference transcripts Batch evaluation across datasets Why it matters: Moves the project from “assignment” → “research tool”.
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Command-Line Interface (CLI) Run evaluations using CLI arguments: python evaluate.py --data data/ --metric wer Why it matters: Signals engineering maturity.
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Integration with ASR Models Directly evaluate outputs from: OpenAI Whisper Google Speech-to-Text Vosk / Wav2Vec2 Why it matters: This connects ML theory to real systems.