3 accepted papers
Text-to-music (TTM) generation, which converts textual descriptions into audio, opens up innovative avenues for multimedia creation. Achieving high quality and diversity in this process demands extensive, high-quality data, which are often scarce in available datasets. Most open-source datasets freq
Deep learning (DL)-based speaker diarization methods have proven powerful performance comparing to traditional clustering-based methods for multi-talker speech diarization and recognition in farfield scenes. However, most DL-based approaches cannot utilize the spatial information well due to the poo…
In this paper, we design a two-stage single-channel speaker-dependent speech separation approach for the CHiME-5 Challenge, targeting the problem of far-field and multi-talker conversational speech recognition in dinner party scenarios involving background noises, reverberations and overlapping spee…