23 citations · 69 across the 7 of their papers we have counts for
7 papers
Interleaved Speech Language Models Latently Work In Text
Talia Sternberg, Gallil Maimon, Yossi Adi
Speech language models (SLMs) increasingly combine speech and text, often by interleaving their tokens within a single sequence. Yet how these two modalities interact in the model'…
Analysing Discrete Self Supervised Speech Representation for Spoken Language Modeling
Amitay Sicherman, Yossi Adi
This work profoundly analyzes discrete self-supervised speech representations (units) through the eyes of Generative Spoken Language Modeling (GSLM). Following the findings of such…
I Hear Your True Colors: Image Guided Audio Generation
Roy Sheffer, Yossi Adi
We propose Im2Wav, an image guided open-domain audio generation system. Given an input image or a sequence of images, Im2Wav generates a semantically relevant sound. Im2Wav is base…
RemixIT: Continual self-training of speech enhancement models via bootstrapped remixing
Efthymios Tzinis, Yossi Adi, Vamsi Krishna Ithapu +3
We present RemixIT, a simple yet effective self-supervised method for training speech enhancement without the need of a single isolated in-domain speech nor a noise waveform. Our a…
Continual self-training with bootstrapped remixing for speech enhancement
Efthymios Tzinis, Yossi Adi, Vamsi K. Ithapu +2
We propose RemixIT, a simple and novel self-supervised training method for speech enhancement. The proposed method is based on a continuously self-training scheme that overcomes li…
Online Self-Attentive Gated RNNs for Real-Time Speaker Separation
Ori Kabeli, Yossi Adi, Zhenyu Tang +2
Deep neural networks have recently shown great success in the task of blind source separation, both under monaural and binaural settings. Although these methods were shown to produ…
SAGRNN: Self-Attentive Gated RNN for Binaural Speaker Separation with Interaural Cue Preservation
Ke Tan, Buye Xu, Anurag Kumar +2
Most existing deep learning based binaural speaker separation systems focus on producing a monaural estimate for each of the target speakers, and thus do not preserve the interaura…