Speech enhancement is the task of taking a noisy speech input and producing an enhanced speech output.
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This paper describes a new baseline system for automatic speech recognition (ASR) in the CHiME-4 challenge to promote the development of noisy ASR in speech processing communities by providing 1) state-of-the-art system with a simplified single system comparable to the complicated top systems in the challenge, 2) publicly available and reproducible recipe through the main repository in the Kaldi speech recognition toolkit.
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.
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.
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.
First, we study the effectiveness of different dereverberation networks (the generator in GAN) and find that LSTM leads a significant improvement as compared with feed-forward DNN and CNN in our dataset.
We study the use of the Wave-U-Net architecture for speech enhancement, a model introduced by Stoller et al for the separation of music vocals and accompaniment.
Many speech enhancement methods try to learn the relationship between noisy and clean speech, obtained using an acoustic room simulator.