Search Results for author:
Found 10 papers, 1 papers with code
CoVoMix: Advancing Zero-Shot Speech Generation for Human-like Multi-talker Conversations
CoVoMix is capable of first converting dialogue text into multiple streams of discrete tokens, with each token stream representing semantic information for individual talkers.
Adapting Multi-Lingual ASR Models for Handling Multiple Talkers
State-of-the-art large-scale universal speech models (USMs) show a decent automatic speech recognition (ASR) performance across multiple domains and languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Target Sound Extraction with Variable Cross-modality Clues
Automatic target sound extraction (TSE) is a machine learning approach to mimic the human auditory perception capability of attending to a sound source of interest from a mixture of sources.
Target Speaker Voice Activity Detection with Transformers and Its Integration with End-to-End Neural Diarization
This paper describes a speaker diarization model based on target speaker voice activity detection (TS-VAD) using transformers.
Leveraging Real Conversational Data for Multi-Channel Continuous Speech Separation
In this paper, we propose a three-stage training scheme for the CSS model that can leverage both supervised data and extra large-scale unsupervised real-world conversational data.
PickNet: Real-Time Channel Selection for Ad Hoc Microphone Arrays
Since PickNet utilizes only limited acoustic context at each time frame, the system using the proposed model works in real time and is robust to changes in acoustic conditions.
All-neural beamformer for continuous speech separation
Recently, the all deep learning MVDR (ADL-MVDR) model was proposed for neural beamforming and demonstrated superior performance in a target speech extraction task using pre-segmented input.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
VarArray: Array-Geometry-Agnostic Continuous Speech Separation
Continuous speech separation using a microphone array was shown to be promising in dealing with the speech overlap problem in natural conversation transcription.
Continuous Speech Separation with Ad Hoc Microphone Arrays
Prior studies show, with a spatial-temporalinterleaving structure, neural networks can efficiently utilize the multi-channel signals of the ad hoc array.
Neural Speech Separation Using Spatially Distributed Microphones
The inter-channel processing layers apply a self-attention mechanism along the channel dimension to exploit the information obtained with a varying number of microphones.
