This survey paper provides a comprehensive overview of the recent advancements and
challenges in applying large language models to the field of audio signal processing. Audio …

We introduce a state-of-the-art real-time, high-fidelity, audio codec leveraging neural
networks. It consists in a streaming encoder-decoder architecture with quantized latent …

Abstract Language models have been successfully used to model natural signals, such as
images, speech, and music. A key component of these models is a high quality neural …

Recent advancements in generative speech models based on audio-text prompts have
enabled remarkable innovations like high-quality zero-shot text-to-speech. However …

We introduce Moshi, a speech-text foundation model and full-duplex spoken dialogue
framework. Current systems for spoken dialogue rely on pipelines of independent …

Local and non-local attention operations are two ubiquitous operations in the domain of
speech enhancement (SE), and they are effective to generate more discriminative patterns …

Recently, convolution-augmented transformer (Conformer) has achieved promising
performance in automatic speech recognition (ASR) and time-domain speech enhancement …

The ICASSP 2023 Acoustic Echo Cancellation Challenge is intended to stimulate research
in acoustic echo cancellation (AEC), which is an important area of speech enhancement and …

The task of the challenge is to develop a voice anonymization system for speech data which
conceals the speaker's voice identity while protecting linguistic content and emotional states …

Convolutional recurrent networks (CRN) integrating a convolutional encoder-decoder (CED)
structure and a recurrent structure have achieved promising performance for monaural …