Active speaker detection (ASD) seeks to detect who is speaking in a visual scene of one or more speakers. The successful ASD depends on accurate interpretation of short-term and long-term audio and visual information, as well as audio-visual interaction. Unlike the prior work where systems make decision instantaneously using short-term features, we propose a novel framework, named TalkNet, that makes decision by taking both short-term and long-term features into consideration. TalkNet consists of audio and visual temporal encoders for feature representation, audio-visual cross-attention mechanism for inter-modality interaction, and a self-attention mechanism to capture long-term speaking evidence. The experiments demonstrate that TalkNet achieves 3.5% and 2.2% improvement over the state-of-the-art systems on the AVA-ActiveSpeaker dataset and Columbia ASD dataset, respectively. Code has been made available at: https://github.com/TaoRuijie/TalkNet_ASD. CCS CONCEPTS• Information systems → Speech / audio search.

No abstract

Most of the prior studies in the spatial Direction of Arrival (DoA) domain focus on a single modality. However, humans use auditory and visual senses to detect the presence of sound sources. With this motivation, we propose to use neural networks with audio and visual signals for multi-speaker localization. The use of heterogeneous sensors can provide complementary information to overcome uni-modal challenges, such as noise, reverberation, illumination variations, and occlusions. We attempt to address these issues by introducing an adaptive weighting mechanism for audio-visual fusion. We also propose a novel video simulation method that generates visual features from noisy target 3D annotations that are synchronized with acoustic features. Experimental results confirm that audio-visual fusion consistently improves the performance of speaker DoA estimation, while the adaptive weighting mechanism shows clear benefits.

A speaker extraction algorithm seeks to extract the speech of a target speaker from a multi-talker speech mixture when given a cue that represents the target speaker, such as a pre-enrolled speech utterance, or an accompanying video track. Visual cues are particularly useful when a pre-enrolled speech is not available. In this work, we don't rely on the target speaker's pre-enrolled speech, but rather use the target speaker's face track as the speaker cue, that is referred to as the auxiliary reference, to form an attractor towards the target speaker. We advocate that the temporal synchronization between the speech and its accompanying lip movements is a direct and dominant audio-visual cue. Therefore, we propose a self-supervised pretraining strategy, to exploit the speech-lip synchronization cue for target speaker extraction, which allows us to leverage abundant unlabeled in-domain data. We transfer the knowledge from the pre-trained model to the attractor encoder of the speaker extraction network. We show that the proposed speaker extraction network outperforms various competitive baselines in terms of signal quality, perceptual quality, and intelligibility, achieving state-of-the-art performance.

Speaker extraction algorithm relies on the speech sample from the target speaker as the reference point to focus its attention. Such a reference speech is typically pre-recorded. On the other hand, the temporal synchronization between speech and lip movement also serves as an informative cue. Motivated by this idea, we study a novel technique to use speech-lip visual cues to extract reference target speech directly from mixture speech during inference time, without the need of pre-recorded reference speech. We propose a multi-modal speaker extraction network, named MuSE, that is conditioned only on a lip image sequence. MuSE not only outperforms other competitive baselines in terms of SI-SDR and PESQ, but also shows consistent improvement in cross-dataset evaluations.

A speaker extraction algorithm seeks to extract the target speaker's voice from a multi-talker speech mixture. An auxiliary reference, such as a video recording or a pre-recorded speech, is usually used as a cue to form a top-down auditory attention. The prior studies are focused mostly on speaker extraction from a multi-talker speech mixture with highly overlapping speakers. However, a multi-talker speech mixture is often sparsely overlapped, furthermore, the target speaker could even be absent sometimes. In this paper, we propose a universal speaker extraction network that works for all multi-talker scenarios, where the target speaker can be either absent or present. When the target speaker is present, the network performs over a wide range of target-interference speaker overlapping ratios, from 0% to 100%. The speech in such universal multi-talker scenarios is generally described as sparsely overlapped speech. We advocate that a visual cue, i.e. lips movement, is more informative to serve as the auxiliary reference than an audio cue, i.e. pre-recorded speech. In addition, we propose a scenario-aware differentiated loss function for network training. The experimental results show that our proposed network outperforms various competitive baselines in disentangling sparsely overlapped speech in terms of signal fidelity and perceptual evaluations.

A speaker extraction algorithm seeks to extract the target speaker's speech from a multi-talker speech mixture. The prior studies focus mostly on speaker extraction from a highly overlapped multi-talker speech mixture. However, the targetinterference speaker overlapping ratios could vary over a wide range from 0% to 100% in natural speech communication, furthermore, the target speaker could be absent in the speech mixture, the speech mixtures in such universal multi-talker scenarios are described as general speech mixtures. The speaker extraction algorithm requires an auxiliary reference, such as a video recording or a pre-recorded speech, to form top-down auditory attention on the target speaker. We advocate that a visual cue, i.e., lip movement, is more informative than an audio cue, i.e., pre-recorded speech, to serve as the auxiliary reference for speaker extraction in disentangling the target speaker from a general speech mixture. In this paper, we propose a universal speaker extraction network with a visual cue, that works for all multi-talker scenarios. In addition, we propose a scenarioaware differentiated loss function for network training, to balance the network performance over different target-interference speaker pairing scenarios. The experimental results show that our proposed method outperforms various competitive baselines for general speech mixtures in terms of signal fidelity.

Speaker extraction seeks to extract the clean speech of a target speaker from a multi-talker mixture speech. There have been studies to use a pre-recorded speech sample or face image of the target speaker as the speaker cue. In human communication, co-speech gestures that are naturally timed with speech also contribute to speech perception. In this work, we explore the use of co-speech gestures sequence, e.g. hand and body movements, as the speaker cue for speaker extraction, which could be easily obtained from low-resolution video recordings, thus more available than face recordings. We propose two networks using the co-speech gestures cue to perform attentive listening on the target speaker, one that implicitly fuses the cospeech gestures cue in the speaker extraction process, the other performs speech separation first, followed by explicitly using the co-speech gestures cue to associate a separated speech to the target speaker. The experimental results show that the co-speech gestures cue is informative in associating with the target speaker.