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Preparing voice dataset for speech recognition involves collection, annotation, cleaning, feature extraction, splitting, and preprocessing to ensure the model learns effectively from diverse and representative samples.
Data Collection:
Gather diverse audio samples representing various speakers, accents, and environmental conditions, ensuring coverage of different languages and speech styles.
Data Annotation:
Transcribe audio recordings into text, annotating timestamps, speaker information, and metadata like background noise levels and recording quality.
Data Cleaning:
Remove irrelevant segments like silence or background noise, normalize audio for consistent volume levels, and eliminate distortions or artifacts.
Feature Extraction:
Convert audio signals into numerical representations using techniques like MFCCs or spectrograms, extracting relevant features such as phonemes or words.
Data Splitting:
Divide the dataset into training, validation, and test sets while preserving the distribution of speakers and languages.
Preprocessing:
Apply normalization, filtering, and resampling to preprocess audio data, along with data augmentation and feature extraction to enhance model robustness and generalization.
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