USpeech: Ultrasound-Enhanced Speech with Minimal Human Effort via Cross-Modal Synthesis

Speech enhancement is crucial in human-computer interaction, especially for ubiquitous devices. Ultrasound-based speech enhancement has emerged as an attractive choice because of its superior ubiquity and performance. However, inevitable interference from unexpected and unintended sources during audio-ultrasound data acquisition makes existing solutions rely heavily on human effort for data collection and processing. This leads to significant data scarcity that limits the full potential of ultrasound-based speech enhancement. To address this, we propose USpeech, a cross-modal ultrasound synthesis framework for speech enhancement with minimal human effort. At its core is a two-stage framework that establishes correspondence between visual and ultrasonic modalities by leveraging audible audio as a bridge. This approach overcomes challenges from the lack of paired video-ultrasound datasets and the inherent heterogeneity between video and ultrasound data. Our framework incorporates contrastive video-audio pre-training to project modalities into a shared semantic space and employs an audio-ultrasound encoder-decoder for ultrasound synthesis. We then present a speech enhancement network that enhances speech in the time-frequency domain and recovers the clean speech waveform via a neural vocoder. Comprehensive experiments show USpeech achieves remarkable performance using synthetic ultrasound data comparable to physical data, significantly outperforming state-of-the-art ultrasound-based speech enhancement baselines. USpeech is open-sourced at https://github.com/aiot-lab/USpeech/.

Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning

Federated learning (FL) inevitably confronts the challenge of system heterogeneity in practical scenarios. To enhance the capabilities of most model-homogeneous FL methods in handling system heterogeneity, we propose a training scheme that can extend their capabilities to cope with this challenge. In this paper, we commence our study with a detailed exploration of homogeneous and heterogeneous FL settings and discover three key observations: (1) a positive correlation between client performance and layer similarities, (2) higher similarities in the shallow layers in contrast to the deep layers, and (3) the smoother gradients distributions indicate the higher layer similarities. Building upon these observations, we propose InCo Aggregation that leverags internal cross-layer gradients, a mixture of gradients from shallow and deep layers within a server model, to augment the similarity in the deep layers without requiring additional communication between clients. Furthermore, our methods can be tailored to accommodate model-homogeneous FL methods such as FedAvg, FedProx, FedNova, Scaffold, and MOON, to expand their capabilities to handle the system heterogeneity. Copious experimental results validate the effectiveness of InCo Aggregation, spotlighting internal cross-layer gradients as a promising avenue to enhance the performance in heterogenous FL.

Radio2Text: Streaming Speech Recognition Using mmWave Radio Signals

Millimeter wave (mmWave) based speech recognition provides more possibility for audio-related applications, such as conference speech transcription and eavesdropping. However, considering the practicality in real scenarios, latency and recognizable vocabulary size are two critical factors that cannot be overlooked. In this paper, we propose Radio2Text, the first mmWave-based system for streaming automatic speech recognition (ASR) with a vocabulary size exceeding 13,000 words. Radio2Text is based on a tailored streaming Transformer that is capable of effectively learning representations of speech-related features, paving the way for streaming ASR with a large vocabulary. To alleviate the deficiency of streaming networks unable to access entire future inputs, we propose the Guidance Initialization that facilitates the transfer of feature knowledge related to the global context from the non-streaming Transformer to the tailored streaming Transformer through weight inheritance. Further, we propose a cross-modal structure based on knowledge distillation (KD), named cross-modal KD, to mitigate the negative effect of low quality mmWave signals on recognition performance. In the cross-modal KD, the audio streaming Transformer provides feature and response guidance that inherit fruitful and accurate speech information to supervise the training of the tailored radio streaming Transformer. The experimental results show that our Radio2Text can achieve a character error rate of 5.7% and a word error rate of 9.4% for the recognition of a vocabulary consisting of over 13,000 words.

Radio2Speech: High Quality Speech Recovery from Radio Frequency Signals

Considering the microphone is easily affected by noise and soundproof materials, the radio frequency (RF) signal is a promising candidate to recover audio as it is immune to noise and can traverse many soundproof objects. In this paper, we introduce Radio2Speech, a system that uses RF signals to recover high quality speech from the loudspeaker. Radio2Speech can recover speech comparable to the quality of the microphone, advancing from recovering only single tone music or incomprehensible speech in existing approaches. We use Radio UNet to accurately recover speech in time-frequency domain from RF signals with limited frequency band. Also, we incorporate the neural vocoder to synthesize the speech waveform from the estimated time-frequency representation without using the contaminated phase. Quantitative and qualitative evaluations show that in quiet, noisy and soundproof scenarios, Radio2Speech achieves state-of-the-art performance and is on par with the microphone that works in quiet scenarios.