Speech enhancement is the task of taking a noisy speech input and producing an enhanced speech output.
( Image credit: A Fully Convolutional Neural Network For Speech Enhancement )
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In contrast to current techniques, we operate at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them.
In this paper, we present a novel system that separates the voice of a target speaker from multi-speaker signals, by making use of a reference signal from the target speaker.
MMSE approaches utilising the proposed a priori SNR estimator are able to achieve higher enhanced speech quality and intelligibility scores than recent masking- and mapping-based deep learning approaches.
Most methods of voice restoration for patients suffering from aphonia either produce whispered or monotone speech.
In this work, we present the results of adapting a speech enhancement generative adversarial network by finetuning the generator with small amounts of data.
The dataset contains temporally labeled face tracks in video, where each face instance is labeled as speaking or not, and whether the speech is audible.
In hearing aids, the presence of babble noise degrades hearing intelligibility of human speech greatly.
In this challenge, we open-source a large clean speech and noise corpus for training the noise suppression models and a representative test set to real-world scenarios consisting of both synthetic and real recordings.
Despite the significant progress made in the last years, state-of-the-art speech recognition technologies provide a satisfactory performance only in the close-talking condition.