When video is shot in noisy environment, the voice of a speaker seen in the video can be enhanced using the visible mouth movements, reducing background noise. While most existing methods use audio-only inputs, improved performance is obtained with our visual speech enhancement, based on an audio-visual neural network. We include in the training data videos to which we added the voice of the target speaker as background noise. Since the audio input is not sufficient to separate the voice of a speaker from his own voice, the trained model better exploits the visual input and generalizes well to different noise types. The proposed model outperforms prior audio visual methods on two public lipreading datasets. It is also the first to be demonstrated on a dataset not designed for lipreading, such as the weekly addresses of Barack Obama.
Traditional speech enhancement methods include spectral restoration @cite_36 @cite_26 , Wiener filtering @cite_30 and statistical model-based methods @cite_31 . Recently, deep neural networks have been adopted for speech enhancement @cite_12 @cite_19 @cite_13 , generally outperforming the traditional methods @cite_24 .
@cite_30 @cite_26 @cite_36 @cite_24 @cite_19 @cite_31 @cite_13 @cite_12
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This paper presents an application of missing data techniques in speech enhancement. The enhancement system consists of two stages: the first stage uses a recurrent neural network, which is supplied with noisy speech and produces enhanced speech; whereas the second stage uses missing data techniques to further improve the quality of enhanced speech. The results suggest that combining missing data technique with RNN enhancement is an effective enhancement scheme resulting in a 16 dB background noise reduction for all input signal to noise ratio (SNR) conditions from -5 to 20 dB, improved spectral quality and robust automatic speech recognition performance. Current speech enhancement techniques operate on the spectral domain and or exploit some higher-level feature. The majority of them tackle a limited number of noise conditions and rely on first-order statistics. To circumvent these issues, deep networks are being increasingly used, thanks to their ability to learn complex functions from large example sets. In this work, we propose the use of generative adversarial networks for speech enhancement. 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. We evaluate the proposed model using an independent, unseen test set with two speakers and 20 alternative noise conditions. The enhanced samples confirm the viability of the proposed model, and both objective and subjective evaluations confirm the effectiveness of it. With that, we open the exploration of generative architectures for speech enhancement, which may progressively incorporate further speech-centric design choices to improve their performance. This paper focuses on the class of speech enhancement systems which capitalize on the major importance of the short-time spectral amplitude (STSA) of the speech signal in its perception. A system which utilizes a minimum mean-square error (MMSE) STSA estimator is proposed and then compared with other widely used systems which are based on Wiener filtering and the "spectral subtraction" algorithm. In this paper we derive the MMSE STSA estimator, based on modeling speech and noise spectral components as statistically independent Gaussian random variables. We analyze the performance of the proposed STSA estimator and compare it with a STSA estimator derived from the Wiener estimator. We also examine the MMSE STSA estimator under uncertainty of signal presence in the noisy observations. In constructing the enhanced signal, the MMSE STSA estimator is combined with the complex exponential of the noisy phase. It is shown here that the latter is the MMSE estimator of the complex exponential of the original phase, which does not affect the STSA estimation. The proposed approach results in a significant reduction of the noise, and provides enhanced speech with colorless residual noise. The complexity of the proposed algorithm is approximately that of other systems in the discussed class. In this paper, we propose a statistical model for speech enhancement that takes into account the time-correlation between successive speech spectral components. It retains the simplicity associated with the Gaussian statistical model, and enables the extension of existing algorithms to noncausal estimation. The sequence of speech spectral variances is a random process, which is generally correlated with the sequence of speech spectral magnitudes. Causal and noncausal estimators for the a priori SNR are derived in agreement with the model assumptions and the estimation of the speech spectral components. We show that a special case of the causal estimator degenerates to a "decision-directed" estimator with a time-varying frequency-dependent weighting factor. Experimental results demonstrate the improved performance of the proposed algorithms.