Search Results for author:
Found 112 papers, 40 papers with code
t-SOT FNT: Streaming Multi-talker ASR with Text-only Domain Adaptation Capability
Token-level serialized output training (t-SOT) was recently proposed to address the challenge of streaming multi-talker automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
SpeechX: Neural Codec Language Model as a Versatile Speech Transformer
Recent advancements in generative speech models based on audio-text prompts have enabled remarkable innovations like high-quality zero-shot text-to-speech.
MACO: A Modality Adversarial and Contrastive Framework for Modality-missing Multi-modal Knowledge Graph Completion
Nevertheless, existing methods emphasize the design of elegant KGC models to facilitate modality interaction, neglecting the real-life problem of missing modalities in KGs.
ELFNet: Evidential Local-global Fusion for Stereo Matching
Although existing stereo matching models have achieved continuous improvement, they often face issues related to trustworthiness due to the absence of uncertainty estimation.
Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment
As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to identify identical entities across disparate knowledge graphs (KGs) by exploiting associated visual information.
Ranked #1 on
Multi-modal Entity Alignment
on UMVM-oea-d-w-v2
(using extra training data)
On decoder-only architecture for speech-to-text and large language model integration
Large language models (LLMs) have achieved remarkable success in the field of natural language processing, enabling better human-computer interaction using natural language.
AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology
In this pioneering study, inspired by AutoGPT, the state-of-the-art open-source application based on the GPT-4 large language model, we develop a novel tool called AD-AutoGPT which can conduct data collection, processing, and analysis about complex health narratives of Alzheimer's Disease in an autonomous manner via users' textual prompts.
Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models
Large Language Models (LLMs), with their remarkable task-handling capabilities and innovative outputs, have catalyzed significant advancements across a spectrum of fields.
Catalytic activity prediction
Chemical-Disease Interaction Extraction
+14
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
VioLA: Unified Codec Language Models for Speech Recognition, Synthesis, and Translation
Recent research shows a big convergence in model architecture, training objectives, and inference methods across various tasks for different modalities.
Newton-Cotes Graph Neural Networks: On the Time Evolution of Dynamic Systems
Reasoning system dynamics is one of the most important analytical approaches for many scientific studies.
Revisit and Outstrip Entity Alignment: A Perspective of Generative Models
We then reveal that their incomplete objective limits the capacity on both entity alignment and entity synthesis (i. e., generating new entities).
Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representations
Moreover, a Knowledge-Enhance Encoder (KEE) is proposed to leverage SGK as input to further enhance structured representations.
HyperStyle3D: Text-Guided 3D Portrait Stylization via Hypernetworks
While the use of 3D-aware GANs bypasses the requirement of 3D data, we further alleviate the necessity of style images with the CLIP model being the stylization guidance.
ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation
Quantum many-body physics simulation has important impacts on understanding fundamental science and has applications to quantum materials design and quantum technology.
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.
Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modeling
We propose a cross-lingual neural codec language model, VALL-E X, for cross-lingual speech synthesis.
View Consistency Aware Holistic Triangulation for 3D Human Pose Estimation
The rapid development of multi-view 3D human pose estimation (HPE) is attributed to the maturation of monocular 2D HPE and the geometry of 3D reconstruction.
Speaker Change Detection for Transformer Transducer ASR
Speaker change detection (SCD) is an important feature that improves the readability of the recognized words from an automatic speech recognition (ASR) system by breaking the word sequence into paragraphs at speaker change points.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Domain-Agnostic Molecular Generation with Self-feedback
To this end, we introduce MolGen, a pre-trained molecular language model tailored specifically for molecule generation.
Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
In addition, we find Vall-E could preserve the speaker's emotion and acoustic environment of the acoustic prompt in synthesis.
Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo
To detect more anchor pixels to ensure better adaptive patch deformation, we propose to evaluate the matching ambiguity of a certain pixel by checking the convergence of the estimated depth as optimization proceeds.
MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid
Multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) whose entities are associated with relevant images.
Ranked #1 on
Entity Alignment
on FBYG15k
(using extra training data)
BEATs: Audio Pre-Training with Acoustic Tokenizers
In the first iteration, we use random projection as the acoustic tokenizer to train an audio SSL model in a mask and label prediction manner.
Ranked #1 on
Audio Classification
on ESC-50
(using extra training data)
Simulating 2+1D Lattice Quantum Electrodynamics at Finite Density with Neural Flow Wavefunctions
We present a neural flow wavefunction, Gauge-Fermion FlowNet, and use it to simulate 2+1D lattice compact quantum electrodynamics with finite density dynamical fermions.
Exploring WavLM on Speech Enhancement
There is a surge in interest in self-supervised learning approaches for end-to-end speech encoding in recent years as they have achieved great success.
An Adapter based Multi-label Pre-training for Speech Separation and Enhancement
In recent years, self-supervised learning (SSL) has achieved tremendous success in various speech tasks due to its power to extract representations from massive unlabeled data.
Handling Trade-Offs in Speech Separation with Sparsely-Gated Mixture of Experts
Employing a monaural speech separation (SS) model as a front-end for automatic speech recognition (ASR) involves balancing two kinds of trade-offs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Self-supervised learning with bi-label masked speech prediction for streaming multi-talker speech recognition
In this paper, we investigate SSL for streaming multi-talker speech recognition, which generates transcriptions of overlapping speakers in a streaming fashion.
Speech separation with large-scale self-supervised learning
Compared with a supervised baseline and the WavLM-based SS model using feature embeddings obtained with the previously released 94K hours trained WavLM, our proposed model obtains 15. 9% and 11. 2% of relative word error rate (WER) reductions, respectively, for a simulated far-field speech mixture test set.
Simulating realistic speech overlaps improves multi-talker ASR
Multi-talker automatic speech recognition (ASR) has been studied to generate transcriptions of natural conversation including overlapping speech of multiple speakers.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Real-time Speech Interruption Analysis: From Cloud to Client Deployment
Meetings are an essential form of communication for all types of organizations, and remote collaboration systems have been much more widely used since the COVID-19 pandemic.
Tele-Knowledge Pre-training for Fault Analysis
In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge normally found in both machine log data and product documents.
On Robust Cross-View Consistency in Self-Supervised Monocular Depth Estimation
Compared with the photometric consistency loss as well as the rigid point cloud alignment loss, the proposed DFA and VDA losses are more robust owing to the strong representation power of deep features as well as the high tolerance of voxel density to the aforementioned challenges.
VarArray Meets t-SOT: Advancing the State of the Art of Streaming Distant Conversational Speech Recognition
To combine the best of both technologies, we newly design a t-SOT-based ASR model that generates a serialized multi-talker transcription based on two separated speech signals from VarArray.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph
Multi-modal aspect-based sentiment classification (MABSC) is task of classifying the sentiment of a target entity mentioned in a sentence and an image.
LaKo: Knowledge-driven Visual Question Answering via Late Knowledge-to-Text Injection
Visual question answering (VQA) often requires an understanding of visual concepts and language semantics, which relies on external knowledge.
Ranked #14 on
Visual Question Answering (VQA)
on OK-VQA
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning
Specifically, we (1) developed a cross-modal semantic grounding network to investigate the model's capability of disentangling semantic attributes from the images; (2) applied an attribute-level contrastive learning strategy to further enhance the model's discrimination on fine-grained visual characteristics against the attribute co-occurrence and imbalance; (3) proposed a multi-task learning policy for considering multi-model objectives.
Ranked #1 on
Zero-Shot Learning
on CUB-200-2011
Disentangled Ontology Embedding for Zero-shot Learning
In this paper, we focus on ontologies for augmenting ZSL, and propose to learn disentangled ontology embeddings guided by ontology properties to capture and utilize more fine-grained class relationships in different aspects.
Ultra Fast Speech Separation Model with Teacher Student Learning
In this paper, an ultra fast speech separation Transformer model is proposed to achieve both better performance and efficiency with teacher student learning (T-S learning).
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.
Streaming Speaker-Attributed ASR with Token-Level Speaker Embeddings
The proposed speaker embedding, named t-vector, is extracted synchronously with the t-SOT ASR model, enabling joint execution of speaker identification (SID) or speaker diarization (SD) with the multi-talker transcription with low latency.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Knowledge-informed Molecular Learning: A Survey on Paradigm Transfer
To enhance the generation and decipherability of purely data-driven models, scholars have integrated biochemical domain knowledge into these molecular study models.
Streaming Multi-Talker ASR with Token-Level Serialized Output Training
This paper proposes a token-level serialized output training (t-SOT), a novel framework for streaming multi-talker automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A New Image Codec Paradigm for Human and Machine Uses
Meanwhile, an image predictor is designed and trained to achieve the general-quality image reconstruction with the 16-bit gray-scale profile and signal features.
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey
Machine learning especially deep neural networks have achieved great success but many of them often rely on a number of labeled samples for supervision.
Molecular Contrastive Learning with Chemical Element Knowledge Graph
To address these issues, we construct a Chemical Element Knowledge Graph (KG) to summarize microscopic associations between elements and propose a novel Knowledge-enhanced Contrastive Learning (KCL) framework for molecular representation learning.
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.
Separating Long-Form Speech with Group-Wise Permutation Invariant Training
Multi-talker conversational speech processing has drawn many interests for various applications such as meeting transcription.
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks.
One model to enhance them all: array geometry agnostic multi-channel personalized speech enhancement
Experimental results show that the proposed geometry agnostic model outperforms the model trained on a specific microphone array geometry in both speech quality and automatic speech recognition accuracy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Personalized Speech Enhancement: New Models and Comprehensive Evaluation
Our results show that the proposed models can yield better speech recognition accuracy, speech intelligibility, and perceptual quality than the baseline models, and the multi-task training can alleviate the TSOS issue in addition to improving the speech recognition accuracy.
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
UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware Pre-Training
We integrate the proposed methods into the HuBERT framework.
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.
Transcribe-to-Diarize: Neural Speaker Diarization for Unlimited Number of Speakers using End-to-End Speaker-Attributed ASR
Similar to the target-speaker voice activity detection (TS-VAD)-based diarization method, the E2E SA-ASR model is applied to estimate speech activity of each speaker while it has the advantages of (i) handling unlimited number of speakers, (ii) leveraging linguistic information for speaker diarization, and (iii) simultaneously generating speaker-attributed transcriptions.
Continuous Streaming Multi-Talker ASR with Dual-path Transducers
Streaming recognition of multi-talker conversations has so far been evaluated only for 2-speaker single-turn sessions.
Target-speaker Voice Activity Detection with Improved I-Vector Estimation for Unknown Number of Speaker
Target-speaker voice activity detection (TS-VAD) has recently shown promising results for speaker diarization on highly overlapped speech.
Spacetime Neural Network for High Dimensional Quantum Dynamics
We develop a spacetime neural network method with second order optimization for solving quantum dynamics from the high dimensional Schr\"{o}dinger equation.
Zero-shot Visual Question Answering using Knowledge Graph
Incorporating external knowledge to Visual Question Answering (VQA) has become a vital practical need.
Ranked #1 on
Visual Question Answering (VQA)
on F-VQA
Collaboration of Experts: Achieving 80% Top-1 Accuracy on ImageNet with 100M FLOPs
Our method achieves the state-of-the-art performance on ImageNet, 80. 7% top-1 accuracy with 194M FLOPs.
Ranked #531 on
Image Classification
on ImageNet
A Comparative Study of Modular and Joint Approaches for Speaker-Attributed ASR on Monaural Long-Form Audio
Our evaluation on the AMI meeting corpus reveals that after fine-tuning with a small real data, the joint system performs 8. 9--29. 9% better in accuracy compared to the best modular system while the modular system performs better before such fine-tuning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Investigation of Practical Aspects of Single Channel Speech Separation for ASR
Speech separation has been successfully applied as a frontend processing module of conversation transcription systems thanks to its ability to handle overlapped speech and its flexibility to combine with downstream tasks such as automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Modeling and Reasoning in Event Calculus using Goal-Directed Constraint Answer Set Programming
Automated commonsense reasoning is essential for building human-like AI systems featuring, for example, explainable AI.
Human Listening and Live Captioning: Multi-Task Training for Speech Enhancement
Performance analysis is also carried out by changing the ASR model, the data used for the ASR-step, and the schedule of the two update steps.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
End-to-End Speaker-Attributed ASR with Transformer
This paper presents our recent effort on end-to-end speaker-attributed automatic speech recognition, which jointly performs speaker counting, speech recognition and speaker identification for monaural multi-talker audio.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Large-Scale Pre-Training of End-to-End Multi-Talker ASR for Meeting Transcription with Single Distant Microphone
Transcribing meetings containing overlapped speech with only a single distant microphone (SDM) has been one of the most challenging problems for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
Knowledge-aware Zero-Shot Learning: Survey and Perspective
Zero-shot learning (ZSL) which aims at predicting classes that have never appeared during the training using external knowledge (a. k. a.
Dual-Path Modeling for Long Recording Speech Separation in Meetings
A transformer-based dual-path system is proposed, which integrates transform layers for global modeling.
OntoZSL: Ontology-enhanced Zero-shot Learning
The key of implementing ZSL is to leverage the prior knowledge of classes which builds the semantic relationship between classes and enables the transfer of the learned models (e. g., features) from training classes (i. e., seen classes) to unseen classes.
Gauge Invariant and Anyonic Symmetric Autoregressive Neural Networks for Quantum Lattice Models
Symmetries such as gauge invariance and anyonic symmetry play a crucial role in quantum many-body physics.
Continuous Speech Separation Using Speaker Inventory for Long Multi-talker Recording
Leveraging additional speaker information to facilitate speech separation has received increasing attention in recent years.
Minimum Bayes Risk Training for End-to-End Speaker-Attributed ASR
Recently, an end-to-end speaker-attributed automatic speech recognition (E2E SA-ASR) model was proposed as a joint model of speaker counting, speech recognition and speaker identification for monaural overlapped speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis
Multi-speaker speech recognition of unsegmented recordings has diverse applications such as meeting transcription and automatic subtitle generation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Don't shoot butterfly with rifles: Multi-channel Continuous Speech Separation with Early Exit Transformer
With its strong modeling capacity that comes from a multi-head and multi-layer structure, Transformer is a very powerful model for learning a sequential representation and has been successfully applied to speech separation recently.
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.
An End-to-end Architecture of Online Multi-channel Speech Separation
Previously, we introduced a sys-tem, calledunmixing, fixed-beamformerandextraction(UFE), that was shown to be effective in addressing the speech over-lap problem in conversation transcription.
Brain Stroke Lesion Segmentation Using Consistent Perception Generative Adversarial Network
The assistant network and the discriminator are employed to jointly decide whether the segmentation results are real or fake.
Continuous Speech Separation with Conformer
Continuous speech separation plays a vital role in complicated speech related tasks such as conversation transcription.
Ranked #1 on
Speech Separation
on LibriCSS
(using extra training data)
Investigation of End-To-End Speaker-Attributed ASR for Continuous Multi-Talker Recordings
However, the model required prior knowledge of speaker profiles to perform speaker identification, which significantly limited the application of the model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Deep Multi-Task Learning for Cooperative NOMA: System Design and Principles
We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner.
Joint Speaker Counting, Speech Recognition, and Speaker Identification for Overlapped Speech of Any Number of Speakers
We propose an end-to-end speaker-attributed automatic speech recognition model that unifies speaker counting, speech recognition, and speaker identification on monaural overlapped speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
PuppeteerGAN: Arbitrary Portrait Animation With Semantic-Aware Appearance Transformation
Portrait animation, which aims to animate a still portrait to life using poses extracted from target frames, is an important technique for many real-world entertainment applications.
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.
Generative Adversarial Zero-shot Learning via Knowledge Graphs
However, the side information of classes used now is limited to text descriptions and attribute annotations, which are in short of semantics of the classes.
Continuous speech separation: dataset and analysis
In this paper, we define continuous speech separation (CSS) as a task of generating a set of non-overlapped speech signals from a \textit{continuous} audio stream that contains multiple utterances that are \emph{partially} overlapped by a varying degree.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Advances in Online Audio-Visual Meeting Transcription
This increases marginally to 1. 6% when 50% of the attendees are unknown to the system.
Sequential Multi-Frame Neural Beamforming for Speech Separation and Enhancement
This work introduces sequential neural beamforming, which alternates between neural network based spectral separation and beamforming based spatial separation.
End-to-end Microphone Permutation and Number Invariant Multi-channel Speech Separation
An important problem in ad-hoc microphone speech separation is how to guarantee the robustness of a system with respect to the locations and numbers of microphones.
Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation
Recent studies in deep learning-based speech separation have proven the superiority of time-domain approaches to conventional time-frequency-based methods.
Ranked #15 on
Speech Separation
on WSJ0-2mix
A Learning-Based Two-Stage Spectrum Sharing Strategy with Multiple Primary Transmit Power Levels
Then, based on a novel normalized power level alignment metric, we propose two prediction-transmission structures, namely periodic and non-periodic, for spectrum access (the second part in Stage II), which enable the secondary transmitter (ST) to closely follow the PT power level variation.
PyKaldi2: Yet another speech toolkit based on Kaldi and PyTorch
While similar toolkits are available built on top of the two, a key feature of PyKaldi2 is sequence training with criteria such as MMI, sMBR and MPE.
ViP: Virtual Pooling for Accelerating CNN-based Image Classification and Object Detection
In this paper, we propose Virtual Pooling (ViP), a model-level approach to improve speed and energy consumption of CNN-based image classification and object detection tasks, with a provable error bound.
Low-Latency Speaker-Independent Continuous Speech Separation
Speaker independent continuous speech separation (SI-CSS) is a task of converting a continuous audio stream, which may contain overlapping voices of unknown speakers, into a fixed number of continuous signals each of which contains no overlapping speech segment.
Understanding the Impact of Label Granularity on CNN-based Image Classification
In this paper, we conduct extensive experiments using various datasets to demonstrate and analyze how and why training based on fine-grain labeling, such as "Persian cat" can improve CNN accuracy on classifying coarse-grain classes, in this case "cat."
Recognizing Overlapped Speech in Meetings: A Multichannel Separation Approach Using Neural Networks
The goal of this work is to develop a meeting transcription system that can recognize speech even when utterances of different speakers are overlapped.
Intermediate Deep Feature Compression: the Next Battlefield of Intelligent Sensing
The recent advances of hardware technology have made the intelligent analysis equipped at the front-end with deep learning more prevailing and practical.
Developing Far-Field Speaker System Via Teacher-Student Learning
In this study, we develop the keyword spotting (KWS) and acoustic model (AM) components in a far-field speaker system.
Speaker-Invariant Training via Adversarial Learning
We propose a novel adversarial multi-task learning scheme, aiming at actively curtailing the inter-talker feature variability while maximizing its senone discriminability so as to enhance the performance of a deep neural network (DNN) based ASR system.
Unsupervised Adaptation with Domain Separation Networks for Robust Speech Recognition
Unsupervised domain adaptation of speech signal aims at adapting a well-trained source-domain acoustic model to the unlabeled data from target domain.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Image Quality Assessment Guided Deep Neural Networks Training
For many computer vision problems, the deep neural networks are trained and validated based on the assumption that the input images are pristine (i. e., artifact-free).
Improving Adherence to Heart Failure Management Guidelines via Abductive Reasoning
A standard approach to managing chronic diseases by medical community is to have a committee of experts develop guidelines that all physicians should follow.
Speaker-independent Speech Separation with Deep Attractor Network
A reference point attractor is created in the embedding space to represent each speaker which is defined as the centroid of the speaker in the embedding space.
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.
Deep attractor network for single-microphone speaker separation
We propose a novel deep learning framework for single channel speech separation by creating attractor points in high dimensional embedding space of the acoustic signals which pull together the time-frequency bins corresponding to each source.
Deep Clustering and Conventional Networks for Music Separation: Stronger Together
Deep clustering is the first method to handle general audio separation scenarios with multiple sources of the same type and an arbitrary number of sources, performing impressively in speaker-independent speech separation tasks.
A Physician Advisory System for Chronic Heart Failure Management Based on Knowledge Patterns
In this paper we describe a physician-advisory system for CHF management that codes the entire set of clinical practice guidelines for CHF using answer set programming.
Single-Channel Multi-Speaker Separation using Deep Clustering
In this paper we extend the baseline system with an end-to-end signal approximation objective that greatly improves performance on a challenging speech separation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Deep clustering: Discriminative embeddings for segmentation and separation
The framework can be used without class labels, and therefore has the potential to be trained on a diverse set of sound types, and to generalize to novel sources.
Ranked #25 on
Speech Separation
on WSJ0-2mix
