Search Results for author:
Found 26 papers, 5 papers with code
Combined Generative and Predictive Modeling for Speech Super-resolution
Specifically, we employ a diffusion-based model that is conditioned on the output of a predictive model.
Leveraging Laryngograph Data for Robust Voicing Detection in Speech
Accurately detecting voiced intervals in speech signals is a critical step in pitch tracking and has numerous applications.
Multi-channel Conversational Speaker Separation via Neural Diarization
To enhance ASR performance in conversational or meeting environments, continuous speaker separation (CSS) is commonly employed.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
NeuralKalman: A Learnable Kalman Filter for Acoustic Echo Cancellation
The robustness of the Kalman filter to double talk and its rapid convergence make it a popular approach for addressing acoustic echo cancellation (AEC) challenges.
Multi-resolution location-based training for multi-channel continuous speech separation
The performance of automatic speech recognition (ASR) systems severely degrades when multi-talker speech overlap occurs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Time-Domain Speech Enhancement for Robust Automatic Speech Recognition
However, speech enhancement has not been established as an effective frontend for robust automatic speech recognition (ASR) in noisy conditions compared to an ASR model trained on noisy speech directly.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
VoiceFixer: A Unified Framework for High-Fidelity Speech Restoration
Speech restoration aims to remove distortions in speech signals.
Neural Vocoder is All You Need for Speech Super-resolution
In this paper, we propose a neural vocoder based speech super-resolution method (NVSR) that can handle a variety of input resolution and upsampling ratios.
Ranked #2 on
Audio Super-Resolution
on VCTK Multi-Speaker
A Conformer Based Acoustic Model for Robust Automatic Speech Recognition
The proposed model builds on the wide residual bi-directional long short-term memory network (WRBN) with utterance-wise dropout and iterative speaker adaptation, but employs a Conformer encoder instead of the recurrent network.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Interpreting Deep Knowledge Tracing Model on EdNet Dataset
With more deep learning techniques being introduced into the knowledge tracing domain, the interpretability issue of the knowledge tracing models has aroused researchers' attention.
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction
The reconstruction module is used for auxiliary learning to improve the noise robustness of the learned representation and thus is not required during inference.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+8
Location-based training for multi-channel talker-independent speaker separation
We further demonstrate the effectiveness of LBT for the separation of four and five concurrent speakers.
Multi-Channel and Multi-Microphone Acoustic Echo Cancellation Using A Deep Learning Based Approach
Building on the deep learning based acoustic echo cancellation (AEC) in the single-loudspeaker (single-channel) and single-microphone setup, this paper investigates multi-channel AEC (MCAEC) and multi-microphone AEC (MMAEC).
Efficient End-to-End Speech Recognition Using Performers in Conformers
On-device end-to-end speech recognition poses a high requirement on model efficiency.
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.
Dense CNN with Self-Attention for Time-Domain Speech Enhancement
Even though the proposed loss is based on magnitudes only, a constraint imposed by noise prediction ensures that the loss enhances both magnitude and phase.
Towards Interpretable Deep Learning Models for Knowledge Tracing
We thus propose to adopt the post-hoc method to tackle the interpretability issue for deep learning based knowledge tracing (DLKT) models.
Divide and Conquer: A Deep CASA Approach to Talker-independent Monaural Speaker Separation
Simultaneous grouping is first performed in each time frame by separating the spectra of different speakers with a permutation-invariantly trained neural network.
Ranked #21 on
Speech Separation
on WSJ0-2mix
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Deep Learning Based Phase Reconstruction for Speaker Separation: A Trigonometric Perspective
This study investigates phase reconstruction for deep learning based monaural talker-independent speaker separation in the short-time Fourier transform (STFT) domain.
End-to-End Speech Separation with Unfolded Iterative Phase Reconstruction
In addition, we train through unfolded iterations of a phase reconstruction algorithm, represented as a series of STFT and inverse STFT layers.
Supervised Speech Separation Based on Deep Learning: An Overview
A more recent approach formulates speech separation as a supervised learning problem, where the discriminative patterns of speech, speakers, and background noise are learned from training data.
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Cocktail Party Processing via Structured Prediction
While human listeners excel at selectively attending to a conversation in a cocktail party, machine performance is still far inferior by comparison.
