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Found 20 papers, 1 papers with code
Binaural Angular Separation Network
We propose a neural network model that can separate target speech sources from interfering sources at different angular regions using two microphones.
TokenSplit: Using Discrete Speech Representations for Direct, Refined, and Transcript-Conditioned Speech Separation and Recognition
The model operates on transcripts and audio token sequences and achieves multiple tasks through masking of inputs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Guided Speech Enhancement Network
Multi-microphone speech enhancement problem is often decomposed into two decoupled steps: a beamformer that provides spatial filtering and a single-channel speech enhancement model that cleans up the beamformer output.
CycleGAN-Based Unpaired Speech Dereverberation
Typically, neural network-based speech dereverberation models are trained on paired data, composed of a dry utterance and its corresponding reverberant utterance.
DF-Conformer: Integrated architecture of Conv-TasNet and Conformer using linear complexity self-attention for speech enhancement
To make the model computationally feasible, we extend the Conformer using linear complexity attention and stacked 1-D dilated depthwise convolution layers.
Sparse, Efficient, and Semantic Mixture Invariant Training: Taming In-the-Wild Unsupervised Sound Separation
The best performance is achieved using larger numbers of output sources, enabled by our efficient MixIT loss, combined with sparsity losses to prevent over-separation.
End-to-End Diarization for Variable Number of Speakers with Local-Global Networks and Discriminative Speaker Embeddings
We present an end-to-end deep network model that performs meeting diarization from single-channel audio recordings.
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.
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
What's All the FUSS About Free Universal Sound Separation Data?
We introduce the Free Universal Sound Separation (FUSS) dataset, a new corpus for experiments in separating mixtures of an unknown number of sounds from an open domain of sound types.
Unsupervised Sound Separation Using Mixture Invariant Training
In such supervised approaches, a model is trained to predict the component sources from synthetic mixtures created by adding up isolated ground-truth sources.
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.
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.
SDR - half-baked or well done?
In speech enhancement and source separation, signal-to-noise ratio is a ubiquitous objective measure of denoising/separation quality.
Sound Audio and Speech Processing
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.
Deep Long Short-Term Memory Adaptive Beamforming Networks For Multichannel Robust Speech Recognition
Further, we use hidden units in the deep LSTM acoustic model to assist in predicting the beamforming filter coefficients.
Deep neural networks for single channel source separation
In the training stage, the training data for the source signals are used to train a DNN.
SUTAV: A Turkish Audio-Visual Database
The main aim of collecting SUTAV database was to obtain a large audio-visual collection of spoken words, numbers and sentences in Turkish language.
Confidence-Based Dynamic Classifier Combination For Mean-Shift Tracking
We use two different classifiers, where one comes from a background modeling method, to generate the weight image and we calculate contributions of the classifiers dynamically using their confidences to generate a final weight image to be used in tracking.
