35 datasets found

The TSP speech database is a dataset of speech recordings.

The dataset used in this paper is the Isolet dataset, which contains 4,000 13-channel audio recordings of 100 speakers.

The PANNS dataset is a large-scale audio classification dataset.

The Freesound dataset consists of 18,873 audio files, each assigned one of the 41 unique audio events from the Google's Audioset Ontology.

Few-shot learning aims to generalize unseen classes that appear during testing but are unavailable during training. The performance of prototypical networks in extreme few-shot...

The AISHELL-1 dataset is a Mandarin speech corpus, consisting of 178 hours of speech, with 11 domains and 400 speakers from different accent areas in China.

The TEDLIUM2 dataset is a large corpus of audio recordings of human speech, with a focus on speech recognition tasks.

The dataset used in the paper is the WSJ0-2mix dataset, which contains 30 hours of training data and 10 hours of validation data generated from the WSJ0 dataset. The speech...

Deep Matrix Approximately Nonlinear Decomposition (DEMAND) employs Adam and an alternative optimization strategy that is well suited to optimize convex/alternative convex...

The WavCaps dataset contains chatGPT-assisted weakly-labeled audio captioning data.

The COVID-19 Identification ResNet (CIdeR) dataset consists of 517 crowdsourced coughing and breathing audio recordings from 355 participants, of which 62 participants had tested...

Speech enhancement dataset

The dataset used in this paper is a collection of phonetically and phonologically local allophonic distribution in English, where voiceless stops surface as aspirated...

The VGGSound dataset is a large-scale audio-visual dataset containing 10,000 10-second video clips with corresponding audio files.

The TIMIT corpus is a widely used benchmark for speech recognition tasks. It contains 3,696 training utterances from 462 speakers, excluding the SA sentences. The core test set...

The dataset used for this study was collected for a mental health assessment project conducted at the University of Maryland School of Medicine in collaboration with the...

The CHiME-4 dataset is a large-scale speech recognition dataset, containing over 2 hours of speech from 6 channels.

The dataset used for the task of mispronunciation detection for second language learners.

The GTZAN dataset is a comprehensive collection of 1000 audio tracks, each 30 seconds long, representing ten diverse music genres.

The LJSpeech-1.1 dataset is a large-scale speech dataset containing approximately 24 hours of single-speaker speech recorded at 22 050 Hz.