Search Results for author:
Found 32 papers, 6 papers with code
RIR-SF: Room Impulse Response Based Spatial Feature for Multi-channel Multi-talker ASR
Multi-channel multi-talker automatic speech recognition (ASR) presents ongoing challenges within the speech community, particularly when confronted with significant reverberation effects.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
UniX-Encoder: A Universal $X$-Channel Speech Encoder for Ad-Hoc Microphone Array Speech Processing
2) Multi-Task Capability: Beyond the single-task focus of previous systems, UniX-Encoder acts as a robust upstream model, adeptly extracting features for diverse tasks including ASR and speaker recognition.
Deep Neural Mel-Subband Beamformer for In-car Speech Separation
While current deep learning (DL)-based beamforming techniques have been proved effective in speech separation, they are often designed to process narrow-band (NB) frequencies independently which results in higher computational costs and inference times, making them unsuitable for real-world use.
NeuralEcho: A Self-Attentive Recurrent Neural Network For Unified Acoustic Echo Suppression And Speech Enhancement
Acoustic echo cancellation (AEC) plays an important role in the full-duplex speech communication as well as the front-end speech enhancement for recognition in the conditions when the loudspeaker plays back.
EEND-SS: Joint End-to-End Neural Speaker Diarization and Speech Separation for Flexible Number of Speakers
In this paper, we present a novel framework that jointly performs three tasks: speaker diarization, speech separation, and speaker counting.
Consistent Training and Decoding For End-to-end Speech Recognition Using Lattice-free MMI
Recently, End-to-End (E2E) frameworks have achieved remarkable results on various Automatic Speech Recognition (ASR) tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization
Conversational bilingual speech encompasses three types of utterances: two purely monolingual types and one intra-sententially code-switched type.
Multi-Channel Multi-Speaker ASR Using 3D Spatial Feature
Experimental results show that 1) the proposed ALL-In-One model achieved a comparable error rate to the pipelined system while reducing the inference time by half; 2) the proposed 3D spatial feature significantly outperformed (31\% CERR) all previous works of using the 1D directional information in both paradigms.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Joint Neural AEC and Beamforming with Double-Talk Detection
We train the proposed model in an end-to-end approach to eliminate background noise and echoes from far-end audio devices, which include nonlinear distortions.
FAST-RIR: Fast neural diffuse room impulse response generator
We present a neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
MIMO Self-attentive RNN Beamformer for Multi-speaker Speech Separation
The spatial self-attention module is designed to attend on the cross-channel correlation in the covariance matrices.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
TeCANet: Temporal-Contextual Attention Network for Environment-Aware Speech Dereverberation
In this paper, we exploit the effective way to leverage contextual information to improve the speech dereverberation performance in real-world reverberant environments.
Multi-channel Multi-frame ADL-MVDR for Target Speech Separation
Many purely neural network based speech separation approaches have been proposed to improve objective assessment scores, but they often introduce nonlinear distortions that are harmful to modern automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Audio-visual Multi-channel Integration and Recognition of Overlapped Speech
Automatic speech recognition (ASR) technologies have been significantly advanced in the past few decades.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Directional ASR: A New Paradigm for E2E Multi-Speaker Speech Recognition with Source Localization
The advantages of D-ASR over existing methods are threefold: (1) it provides explicit speaker locations, (2) it improves the explainability factor, and (3) it achieves better ASR performance as the process is more streamlined.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multi-Channel Speaker Verification for Single and Multi-talker Speech
We find the improvements from speaker-dependent directional features more consistent in multi-talker conditions than clean.
An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation
Speech enhancement and speech separation are two related tasks, whose purpose is to extract either one or more target speech signals, respectively, from a mixture of sounds generated by several sources.
ADL-MVDR: All deep learning MVDR beamformer for target speech separation
Speech separation algorithms are often used to separate the target speech from other interfering sources.
Audio-visual Multi-channel Recognition of Overlapped Speech
Automatic speech recognition (ASR) of overlapped speech remains a highly challenging task to date.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Neural Spatio-Temporal Beamformer for Target Speech Separation
Purely neural network (NN) based speech separation and enhancement methods, although can achieve good objective scores, inevitably cause nonlinear speech distortions that are harmful for the automatic speech recognition (ASR).
Audio and Speech Processing Sound
Multi-modal Multi-channel Target Speech Separation
Target speech separation refers to extracting a target speaker's voice from an overlapped audio of simultaneous talkers.
Enhancing End-to-End Multi-channel Speech Separation via Spatial Feature Learning
Hand-crafted spatial features (e. g., inter-channel phase difference, IPD) play a fundamental role in recent deep learning based multi-channel speech separation (MCSS) methods.
Self-supervised learning for audio-visual speaker diarization
Speaker diarization, which is to find the speech segments of specific speakers, has been widely used in human-centered applications such as video conferences or human-computer interaction systems.
Audio-visual Recognition of Overlapped speech for the LRS2 dataset
Experiments on overlapped speech simulated from the LRS2 dataset suggest the proposed AVSR system outperformed the audio only baseline LF-MMI DNN system by up to 29. 98\% absolute in word error rate (WER) reduction, and produced recognition performance comparable to a more complex pipelined system.
Ranked #4 on
Audio-Visual Speech Recognition
on LRS2
Audio-Visual Speech Recognition
Automatic Speech Recognition (ASR)
+4
Review of Single-cell RNA-seq Data Clustering for Cell Type Identification and Characterization
In recent years, the advances in single-cell RNA-seq techniques have enabled us to perform large-scale transcriptomic profiling at single-cell resolution in a high-throughput manner.
A Unified Framework for Speech Separation
The initial solutions introduced for deep learning based speech separation analyzed the speech signals into time-frequency domain with STFT; and then encoded mixed signals were fed into a deep neural network based separator.
Audio-Visual Speech Separation and Dereverberation with a Two-Stage Multimodal Network
Background noise, interfering speech and room reverberation frequently distort target speech in real listening environments.
Audio and Speech Processing Sound Signal Processing
A comprehensive study of speech separation: spectrogram vs waveform separation
We study the speech separation problem for far-field data (more similar to naturalistic audio streams) and develop multi-channel solutions for both frequency and time-domain separators with utilizing spectral, spatial and speaker location information.
End-to-End Multi-Channel Speech Separation
This paper extended the previous approach and proposed a new end-to-end model for multi-channel speech separation.
Encrypted Speech Recognition using Deep Polynomial Networks
One good property of the DPN is that it can be trained on unencrypted speech features in the traditional way.
Time Domain Audio Visual Speech Separation
Audio-visual multi-modal modeling has been demonstrated to be effective in many speech related tasks, such as speech recognition and speech enhancement.
Audio and Speech Processing Sound
End-to-End Attention based Text-Dependent Speaker Verification
A new type of End-to-End system for text-dependent speaker verification is presented in this paper.
