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Found 10 papers, 4 papers with code
Radar Rainbow Beams For Wideband mmWave Communication: Beam Training And Tracking
Three detection and estimation schemes are proposed based on radar rainbow beams for estimation of the users' angles, distances, and velocities, which are then exploited for communication beamformer design.
ZeroPrompt: Streaming Acoustic Encoders are Zero-Shot Masked LMs
In this paper, we present ZeroPrompt (Figure 1-(a)) and the corresponding Prompt-and-Refine strategy (Figure 3), two simple but effective \textbf{training-free} methods to decrease the Token Display Time (TDT) of streaming ASR models \textbf{without any accuracy loss}.
TrimTail: Low-Latency Streaming ASR with Simple but Effective Spectrogram-Level Length Penalty
In this paper, we present TrimTail, a simple but effective emission regularization method to improve the latency of streaming ASR models.
Error Propagation and Overhead Reduced Channel Estimation for RIS-Aided Multi-User mmWave Systems
In this paper, we propose a novel two-stage based uplink channel estimation strategy with reduced pilot overhead and error propagation for a reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter wave (mmWave) system.
Channel Estimation for RIS-Aided Multi-User mmWave Systems with Uniform Planar Arrays
Specifically, in Stage I, the channel state information (CSI) of a typical user is estimated.
WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit
Recently, we made available WeNet, a production-oriented end-to-end speech recognition toolkit, which introduces a unified two-pass (U2) framework and a built-in runtime to address the streaming and non-streaming decoding modes in a single model.
WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition
In this paper, we present WenetSpeech, a multi-domain Mandarin corpus consisting of 10000+ hours high-quality labeled speech, 2400+ hours weakly labeled speech, and about 10000 hours unlabeled speech, with 22400+ hours in total.
Ranked #5 on
Speech Recognition
on WenetSpeech
U2++: Unified Two-pass Bidirectional End-to-end Model for Speech Recognition
On the experiment of AISHELL-1, we achieve a 4. 63\% character error rate (CER) with a non-streaming setup and 5. 05\% with a streaming setup with 320ms latency by U2++.
WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit
In this paper, we propose an open source, production first, and production ready speech recognition toolkit called WeNet in which a new two-pass approach is implemented to unify streaming and non-streaming end-to-end (E2E) speech recognition in a single model.
