27 datasets found

The QUT-NOISE-TIMIT dataset is a dataset for speech enhancement. It consists of clean speech and noise.

The dataset used for training and validation of the proposed deep binaural MFMVDR filter, comprising simulated binaural room impulse responses and clean speech and noise.

The dataset used for evaluation of the proposed deep binaural MFMVDR filter, comprising measured binaural room impulse responses and clean speech and noise.

The dataset used for training and validation of the proposed deep binaural MFMVDR filter, comprising simulated binaural room impulse responses and clean speech and noise.

Noisy speech database for training speech enhancement algorithms and TTS models.

The MHINT corpus is a Mandarin Chinese speech corpus used for speech recognition and speech enhancement. It contains 480 utterances of 10 speakers, each with 10 seconds of speech.

The proposed self-supervised speech quality estimator trained only on clean speech.

The database in [28] which was used to evaluate SEGAN in [14].

Speech enhancement dataset

Diffusion probabilistic model for speech enhancement

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 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 LRS2 dataset consists of 48,164 video clips from outdoor shows on BBC television. Each video is accompanied by an audio corresponding to a sentence with up to 100 characters.

Speech enhancement deep learning systems usually require large amounts of training data to operate in broad conditions or real applications. This makes the adaptability of those...

Generative models have gained more and more attention in recent years for their remarkable success in tasks that required estimating and sampling data distribution to generate...

Training and test datasets were generated by contaminating the clean data with reverberation and noise.

The dataset used in the paper is a selection of 14 noisy mixtures created manually from the Voice Bank speech corpus.

The dataset used in the paper is a selection of ten British English speakers – both male and female – from the Voice Bank speech corpus, each of which has around 400 clean...

The MultiCorpus dataset is a diverse and acoustically adverse dataset generated by mixing reverberant speech and reverberant noise using speech utterances, noise segments and...

The VoiceBank+DEMAND (VBDMD) 28-speaker dataset is a publicly available and popular benchmark dataset for single-channel speech enhancement.