Audio Classification Models
Audio classification models are machine learning systems for audio classification, the task of assigning one or more labels to an audio recording or to short fragments of it.
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Connectionist Temporal Classification
Connectionist temporal classification (CTC) is a loss function and output layer design for training neural networks to label unsegmented sequences, such as transcribing an audio recording into characters when…
HuBERT
HuBERT (Hidden-Unit BERT) is a self-supervised learning model for speech representation, introduced by researchers at Meta AI (then Facebook AI Research) in 2021 .
SUPERB
SUPERB, which stands for Speech processing Universal PERformance Benchmark, is a comprehensive evaluation framework designed to measure how well self-supervised learning (SSL) models generalize across a…
Speech Recognition
Speech recognition, usually called automatic speech recognition (ASR), is the computational task of converting a spoken-language signal into a sequence of written symbols.
Wav2Vec 2.0
Wav2Vec 2.0 is a self-supervised learning framework for speech representation, developed by the Facebook AI Research (FAIR) group at Meta and introduced in 2020.
Word error rate
Word error rate (WER) is the standard metric for measuring the accuracy of an automatic speech recognition (ASR) system