Darpa Timit Acoustic-Phonetic Continuous Speech Corpus CD-ROM {TIMIT} | NIST

By John S. Garofolo, Lori Faith Lamel, W. Fisher, Jonathan G. Fiscus, D. Pallett, Nancy L. Dahlgren. 1993

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In Evaluation & Benchmarks

Citations in the corpus

Neural Autoregressive Distribution Estimation (2016, methodology): “We also measured the ability of RNADE to model small patches of speech spectrograms, extracted from the TIMIT dataset (Garofolo et al., 1993).”
Benchmarking Deep Reinforcement Learning for Continuous Control (2016, neutral): “Examples include MNIST (LeCun et al., 1998), Caltech101 (Fei-Fei et al., 2006), CIFAR (Krizhevsky & Hinton, 2009), ImageNet (Deng et al., 2009), PASCAL VOC (Everingham et al., 2010), BSDS500 (Martin et al., 2001), SWITCHBOARD (Godfrey et al., 1992), TIMIT (Garofolo et al., 1993), Aurora (Hirsch & Pe”
Effectiveness of self-supervised pre-training for speech recognition (2019, methodology): “(Schneider et al., 2019; Baevski et al., 2019b; van den Oord et al., 2018) use the same 960h Librispeech data as unlabeled pre-training data, however, they use Phone Error Rates (PER) on the 3h TIMIT dataset (Garofolo et al., 1993) as their performance metric.”
vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations (2019, methodology): “We evaluate models on two benchmarks: TIMIT (Garofolo et al., 1993b) is a 5h dataset with phoneme labels and Wall Street Journal (WSJ; Garofolo et al. 1993a) is a 81h dataset for speech recognition.”
wav2vec: Unsupervised Pre-training for Speech Recognition (2019, positive): “On the TIMIT task we use a 7-layer wav2letter++ model with high dropout (§3; Synnaeve et al., 2016).”
LSTM: A Search Space Odyssey (2015, methodology): “We use the TIMIT Speech corpus (Garofolo et al., 1993) for framewise phone classiﬁcation.”
A Clockwork RNN (2014, methodology): “Each sequence contains an audio signal of one spoken word from the TIMIT Speech Recognition Benchmark (Garofolo et al., 1993).”
Time-Frequency Masking in the Complex Domain for Speech Dereverberation and Denoising (2017, positive): “Each of the training RIRs is convolved with 500 utterances from the TIMIT speech corpus [9], using 10 utterances from each of 50 different speakers.”
Lessons from Building Acoustic Models with a Million Hours of Speech (2019, background): “Over the decades, corpora have grown from a few tens of hours of speech (TIDIGITS [5], TIMIT [6], WSJ [7]), to a few hundred hours (Switchboard [8]), to a few thousand hours of speech (Fisher corpus [9]).”