speech2speech summarizer : a tool for summarizing spoken language into concise audio summaries.
Speech2Speech Summarizer uses Python and advanced machine learning techniques to transcribe spoken language into text and then summarize it. The system leverages natural language processing (NLP) to accurately capture and condense spoken content. It is designed for applications like meeting summarization, lecture transcription, and other scenarios where concise, accurate summaries of spoken content are valuable.
Technical Implementation
The Speech2Speech Summarizer employs a sophisticated approach to audio processing and summarization:
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Audio Feature Engineering:
- Utilized librosa for audio processing and feature extraction.
- Implemented spectral analysis to identify pitch, volume, and tonal variations.
- Developed custom algorithms to detect emphasis based on:
- Sudden changes in volume (energy envelope analysis)
- Pitch modulation (fundamental frequency tracking)
- Speaking rate variations (zero-crossing rate analysis)
- These emphasis markers were used to weight the importance of specific segments in the summarization process.
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Dual Summarization Approach:
- Implemented both extractive and abstractive summarization techniques:
a) Extractive Summarization:
- Used TextRank algorithm to identify key sentences based on graph-based ranking.
- Incorporated emphasis markers to adjust sentence importance scores. b) Abstractive Summarization:
- Leveraged a fine-tuned T5 transformer model for generating concise summaries.
- Integrated emphasis information into the model’s attention mechanism.
- Implemented both extractive and abstractive summarization techniques:
a) Extractive Summarization:
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Comparative Study:
- Conducted a comprehensive study to compare the effectiveness of extractive vs. abstractive summaries:
- Metrics used: ROUGE scores, human evaluation for coherence and information retention.
- Results showed that:
- Extractive summaries preserved original phrasing but were sometimes disjointed.
- Abstractive summaries were more coherent but occasionally missed nuanced information.
- Hybrid approach (combining both methods) yielded the best results in most scenarios.
- Conducted a comprehensive study to compare the effectiveness of extractive vs. abstractive summaries:
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Text-to-Speech Conversion:
- Utilised a Text-to-Speech API to convert the final summaries back into speech.
- Implemented prosody transfer techniques to maintain the emphasis and intonation patterns from the original audio in the summarized version.
This technical implementation allowed for the creation of concise, informative audio summaries that not only captured the key content but also preserved the emphasis and tone of the original speech, providing a more nuanced and accurate representation of the spoken content.