Isolating the voice of a specific person while filtering out other voices or background noises is challenging when video is shot in noisy environments, using a single microphone. For example, video conferences from home or office are disturbed by other voices, TV reporting from city streets is mixed with traffic noise, etc. We propose audio-visual methods to isolate the voice of a single speaker and eliminate unrelated sounds. Face motions captured in the video are used to estimate the speaker's voice, which is applied as a filter on the input audio. This approach avoids using mixtures of sounds in the learning process, as the number of such possible mixtures is huge, and would inevitably bias the trained model.
* Audio-only speech enhancement and separation Previous methods for single-channel, or monaural, speech enhancement and separation mostly use audio only input. The common approach generates masking matrices containing time-frequency (TF) components dominated by each speaker @cite_23 @cite_16 . Huang @cite_17 are among the first to use a deep learning-based approach for speaker dependent speech separation.
@cite_16 @cite_23 @cite_17
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This paper proposes an end-to-end approach for single-channel speaker-independent multi-speaker speech separation, where time-frequency (T-F) masking, the short-time Fourier transform (STFT), and its inverse are represented as layers within a deep network. Previous approaches, rather than computing a loss on the reconstructed signal, used a surrogate loss based on the target STFT magnitudes. This ignores reconstruction error introduced by phase inconsistency. In our approach, the loss function is directly defined on the reconstructed signals, which are optimized for best separation. In addition, we train through unfolded iterations of a phase reconstruction algorithm, represented as a series of STFT and inverse STFT layers. While mask values are typically limited to lie between zero and one for approaches using the mixture phase for reconstruction, this limitation is less relevant if the estimated magnitudes are to be used together with phase reconstruction. We thus propose several novel activation functions for the output layer of the T-F masking, to allow mask values beyond one. On the publicly-available wsj0-2mix dataset, our approach achieves state-of-the-art 12.6 dB scale-invariant signal-to-distortion ratio (SI-SDR) and 13.1 dB SDR, revealing new possibilities for deep learning based phase reconstruction and representing a fundamental progress towards solving the notoriously-hard cocktail party problem. We propose the use of a hierarchical, two-stage discriminant transformation for obtaining audio-visual features that improve automatic speech recognition. Linear discriminant analysis (LDA), followed by a maximum likelihood linear transform (MLLT) is first applied to MFCC based audio-only features, as well as on visual only features, obtained by a discrete cosine transform of the video region of interest. Subsequently, a second stage of LDA and MLLT is applied to the concatenation of the resulting single modality features. The obtained audio-visual features are used to train a traditional HMM based speech recognizer. Experiments on the IBM ViaVoice sup TM audio-visual database demonstrate that the proposed feature fusion method improves speaker-independent, large vocabulary, continuous speech recognition (LVCSR) for both clean and noisy audio conditions considered. A 24 relative word error rate reduction over an audio-only system is achieved in the latter case. Abstract : Monaural speech separation is a fundamental problem in robust speech processing. Recently, deep neural network (DNN) based speech separation methods, which predict either clean speech or an ideal time-frequency mask, have demonstrated remarkable performance improvement. However, a single DNN with a given window length does not leverage contextual information sufficiently, and the differences between the two optimization objectives are not well understood. In this paper, we propose to stack ensembles of DNNs, named multi-resolution stacking, to address monaural speech separation. Each DNN in a module of the stack takes the concatenation of original acoustic features and expansion of the soft output of the lower module as its input, and predicts the ideal ratio mask of the target speaker. The DNNs in the same module explore different contexts by employing different window lengths. We have conducted extensive experiments with three speech corpora. The results demonstrate the effectiveness of the proposed method. We have also compared the two optimization objectives systematically and found that predicting the ideal time-frequency mask is more efficient in utilizing clean training speech, while predicting clean speech is less sensitive to SNR variations. The problem of single-channel speaker separation attempts to extract a speech signal uttered by the speaker of interest from a signal containing a mixture of acoustic signals. Most algorithms that deal with this problem are based on masking, wherein unreliable frequency components from the mixed signal spectrogram are suppressed, and the reliable components are inverted to obtain the speech signal from speaker of interest. Most current techniques estimate this mask in a binary fashion, resulting in a hard mask. In this paper, we present two techniques to separate out the speech signal of the speaker of interest from a mixture of speech signals. One technique estimates all the spectral components of the desired speaker. The second technique estimates a soft mask that weights the frequency subbands of the mixed signal. In both cases, the speech signal of the speaker of interest is reconstructed from the complete spectral descriptions obtained. In their native form, these algorithms are computationally expensive. We also present fast factored approximations to the algorithms. Experiments reveal that the proposed algorithms can result in significant enhancement of individual speakers in mixed recordings, consistently achieving better performance than that obtained with hard binary masks.