Google VoxCeleb

Google VoxCeleb was created to provide a comprehensive dataset for speaker identification tasks. It was developed by researchers at the University of Oxford in collaboration with Google in 2017. The dataset aimed to facilitate advancements in speaker recognition, verification, and diarization by offering a diverse range of voices extracted from YouTube videos.

Strengths of Google VoxCeleb include its large-scale dataset, diverse range of voices, and high-quality audio samples. Weaknesses involve potential biases due to celebrity-focused data and limitations in representing non-celebrity voices. Competitors include datasets like LibriSpeech, Common Voice by Mozilla, and VCTK Corpus.

A Google VoxCeleb expert must have skills in machine learning, particularly in speaker recognition and verification. Proficiency in Python and familiarity with libraries such as TensorFlow or PyTorch are essential. Knowledge of audio processing techniques, including feature extraction and signal processing, is crucial. Experience with handling large datasets and understanding of deep learning architectures for voice-related tasks are also important.