TIMIT

TIMIT Acoustic-Phonetic Continuous Speech Corpus

Dataset Information
Modalities
Texts, Speech
Languages
English
License
Unknown
Homepage

Overview

The TIMIT Acoustic-Phonetic Continuous Speech Corpus is a standard dataset used for evaluation of automatic speech recognition systems. It consists of recordings of 630 speakers of 8 dialects of American English each reading 10 phonetically-rich sentences. It also comes with the word and phone-level transcriptions of the speech.

Source: Improving neural networks by preventing co-adaptation of feature detectors
Image Source: https://roboticrun.wordpress.com/2016/06/21/timit-introduction-the-official-doc/

Variants: timit PER, TIMIT, TCD-TIMIT corpus (mixed-speech), DARPA TIMIT

Associated Benchmarks

This dataset is used in 1 benchmark:

Recent Benchmark Submissions

Task Model Paper Date
Speech Recognition wav2vec 2.0 wav2vec 2.0: A Framework for … 2020-06-20
Speech Recognition vq-wav2vec vq-wav2vec: Self-Supervised Learning of Discrete … 2019-10-12
Speech Recognition LAS multitask with indicators sampling Attention model for articulatory features … 2019-07-02
Speech Recognition wav2vec wav2vec: Unsupervised Pre-training for Speech … 2019-04-11
Speech Recognition RNN + Dropout + BatchNorm + Monophone Reg The PyTorch-Kaldi Speech Recognition Toolkit 2018-11-19
Speech Recognition LSTM The PyTorch-Kaldi Speech Recognition Toolkit 2018-11-19
Speech Recognition Li-GRU The PyTorch-Kaldi Speech Recognition Toolkit 2018-11-19
Speech Recognition GRU The PyTorch-Kaldi Speech Recognition Toolkit 2018-11-19
Speech Recognition RNN The PyTorch-Kaldi Speech Recognition Toolkit 2018-11-19
Speech Recognition LiGRU + Dropout + BatchNorm + Monophone Reg The PyTorch-Kaldi Speech Recognition Toolkit 2018-11-19
Speech Recognition LSTM + Dropout + BatchNorm + Monophone Reg The PyTorch-Kaldi Speech Recognition Toolkit 2018-11-19
Speech Recognition GRU + Dropout + BatchNorm + Monophone Reg The PyTorch-Kaldi Speech Recognition Toolkit 2018-11-19
Speech Recognition QCNN-10L-256FM Quaternion Convolutional Neural Networks for … 2018-06-20
Speech Recognition LSNN Long short-term memory and learning-to-learn … 2018-03-26
Speech Recognition Li-GRU + fMLLR features Light Gated Recurrent Units for … 2018-03-26
Speech Recognition Light Gated Recurrent Units Light Gated Recurrent Units for … 2018-03-26
Speech Recognition Soft Monotonic Attention (ours, offline) Online and Linear-Time Attention by … 2017-04-03
Speech Recognition RNN-CRF on 24(x3) MFSC Segmental Recurrent Neural Networks for … 2016-03-01
Speech Recognition Bi-RNN + Attention Attention-Based Models for Speech Recognition 2015-06-24
Speech Recognition Bi-LSTM + skip connections w/ CTC Speech Recognition with Deep Recurrent … 2013-03-22

Research Papers

Recent papers with results on this dataset: