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Found 13 papers, 2 papers with code
Why does Self-Supervised Learning for Speech Recognition Benefit Speaker Recognition?
Recently, self-supervised learning (SSL) has demonstrated strong performance in speaker recognition, even if the pre-training objective is designed for speech recognition.
Large-Scale Streaming End-to-End Speech Translation with Neural Transducers
Neural transducers have been widely used in automatic speech recognition (ASR).
A Conformer Based Acoustic Model for Robust Automatic Speech Recognition
The proposed model builds on a state-of-the-art recognition system using a bi-directional long short-term memory (BLSTM) model with utterance-wise dropout and iterative speaker adaptation, but employs a Conformer encoder instead of the BLSTM network.
Predicting Atlantic Multidecadal Variability
Atlantic Multidecadal Variability (AMV) describes variations of North Atlantic sea surface temperature with a typical cycle of between 60 and 70 years.
Continuous Speech Separation with Recurrent Selective Attention Network
In this paper, we propose to apply recurrent selective attention network (RSAN) to CSS, which generates a variable number of output channels based on active speaker counting.
FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters
To promote the practical application for autonomous floating wastes cleaning, we present FloW, the first dataset for floating waste detection in inland water areas.
Efficient End-to-End Speech Recognition Using Performers in Conformers
On-device end-to-end speech recognition poses a high requirement on model efficiency.
Multitask Training with Text Data for End-to-End Speech Recognition
We propose a multitask training method for attention-based end-to-end speech recognition models.
Speaker Separation Using Speaker Inventories and Estimated Speech
We propose speaker separation using speaker inventories and estimated speech (SSUSIES), a framework leveraging speaker profiles and estimated speech for speaker separation.
Multi-microphone Complex Spectral Mapping for Utterance-wise and Continuous Speech Separation
Although our system is trained on simulated room impulse responses (RIR) based on a fixed number of microphones arranged in a given geometry, it generalizes well to a real array with the same geometry.
Bridging the Gap Between Monaural Speech Enhancement and Recognition with Distortion-Independent Acoustic Modeling
In this study, we analyze the distortion problem, compare different acoustic models, and investigate a distortion-independent training scheme for monaural speech recognition.
Recurrent Deep Stacking Networks for Speech Recognition
This paper presented our work on applying Recurrent Deep Stacking Networks (RDSNs) to Robust Automatic Speech Recognition (ASR) tasks.
Incorporating Language Level Information into Acoustic Models
This paper proposed a class of novel Deep Recurrent Neural Networks which can incorporate language-level information into acoustic models.
