15 datasets found

The dataset is used for investigating the capabilities of a neural spatiospectral filter to represent and exploit spatial information.

The dataset is used for investigating the capabilities of a neural spatiospectral filter to represent and exploit spatial information.

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.

Speech enhancement dataset

Diffusion probabilistic model for speech enhancement

Diffusion models for audio restoration

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.

The LRS3 dataset is a large-scale dataset for visual speech recognition. It consists of thousands of spoken sentences from TED videos.

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 VoiceBank-DEMAND dataset is a standard benchmark for speech denoising systems. It consists of 28 speakers with 4 signal-to-noise ratios (SNR) (15, 10, 5, and 0 dB) and...

Diffusion-based speech enhancement with a weighted generative-supervised learning loss