Leveraging Audio-Visual Modalities to Enhance Target Speech Extraction: A Speech Chain Approach

Conceptos Básicos

The proposed AVSepChain framework leverages the concept of the speech chain to alleviate the modality imbalance issue in audio-visual target speech extraction tasks by dynamically shifting the dominant and conditional roles between audio and visual modalities.

Resumen

The paper introduces the AVSepChain framework, which aims to extract the target speaker's speech from a mixed audio signal by leveraging the target speaker's lip movements as a guiding condition. The framework consists of two stages: speech perception and speech production.
In the speech perception stage, the AV-Separator utilizes the target speaker's lip movements as a conditional modality to extract the target speech from the mixed audio. The audio modality is treated as the dominant modality in this stage.
In the speech production stage, the roles are reversed, with the visual modality becoming the dominant modality and the audio modality serving as the conditional modality. The AV-Synthesizer generates the residual signal of the target speech based on the lip movements, using the preliminary target speech extracted in the first stage as a condition.
To ensure that the generated target speech captures the same semantic information as the lip movements, the authors introduce a contrastive semantic matching loss. This loss aligns the frame-level representations of the generated speech and the lip movements, which are extracted using pre-trained audio and audio-visual models, respectively.
The authors conduct extensive experiments on multiple benchmark datasets for audio-visual target speech extraction, demonstrating the superior performance of the proposed AVSepChain framework compared to state-of-the-art methods. The results highlight the effectiveness of the speech chain concept in addressing the modality imbalance issue and enhancing the overall quality of the extracted target speech.

Estadísticas

The paper reports the following key metrics:

Scale-Invariant Signal-to-Noise Ratio Improvement (SI-SNRi)
Signal-to-Distortion Ratio Improvement (SDRi)
Perceptual Evaluation of Speech Quality (PESQ)
Word Error Rate (WER)

Citas

"By leveraging the audio and video modalities as conditional information for each other, we simulate the speech perception and production processes in the speech chain, thereby alleviating the modality imbalance issue in AV-TSE."
"To ensure that the generated speech of the target speaker possesses the same semantic information as its lip movements, we employ a contrastive semantic matching loss."

Ideas clave extraídas de

Separate in the Speech Chain: Cross-Modal Conditional Audio-Visual Target Speech Extraction

by Zhaoxi Mu,Xi... a las arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12725.pdf

Consultas más profundas

How can the proposed AVSepChain framework be extended to handle more than two speakers in a mixed audio signal?

To extend the AVSepChain framework to handle more than two speakers in a mixed audio signal, several modifications and enhancements can be implemented:

Speaker Diarization: Incorporate speaker diarization techniques to identify and separate the speech of multiple speakers in the mixed audio signal.
Multi-Speaker Attention Mechanism: Implement a multi-speaker attention mechanism that can dynamically attend to different speakers in the audio-visual input.
Speaker Embeddings: Utilize speaker embeddings to represent and differentiate between multiple speakers, enabling the model to extract and synthesize speech from each speaker accurately.
Multi-Modal Fusion: Enhance the cross-modal fusion process to effectively combine audio and visual information from multiple speakers, ensuring that the model can handle the complexity of separating multiple speakers.

How can the AVSepChain framework be adapted to work in real-time scenarios, where the audio and video streams are not perfectly synchronized?

Adapting the AVSepChain framework to work in real-time scenarios with asynchronous audio and video streams involves the following considerations:

Temporal Alignment: Implement techniques for temporal alignment between the audio and video streams, such as dynamic time warping or synchronization algorithms, to ensure that the information from both modalities is synchronized.
Buffering Mechanism: Introduce a buffering mechanism to handle delays between the audio and video streams, allowing the model to process and synchronize the information effectively.
Incremental Processing: Design the framework to process audio and video inputs incrementally, updating the output in real-time as new information becomes available.
Latency Optimization: Optimize the model architecture and processing pipeline to minimize latency and ensure efficient real-time performance without compromising the quality of target speech extraction.

What other types of visual cues, beyond lip movements, could be explored to further enhance the performance of audio-visual target speech extraction?

In addition to lip movements, exploring the following visual cues can further enhance the performance of audio-visual target speech extraction:

Facial Expressions: Facial expressions can provide valuable cues for emotion recognition and speaker identification, contributing to a more comprehensive understanding of the speaker's context.
Gaze Direction: Analyzing the speaker's gaze direction can help in determining the focus of attention and improving the accuracy of target speech extraction in scenarios with multiple speakers or distractions.
Hand Gestures: Hand gestures can convey additional information and context during speech, aiding in disambiguation and enhancing the overall understanding of the speaker's message.
Body Movements: Incorporating information from the speaker's body movements and posture can offer insights into the speaker's engagement level and emotional state, enriching the audio-visual processing for speech extraction.