How to create a voice dataset?

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.