Search Results for author:
Found 26 papers, 4 papers with code
Probing Self-supervised Learning Models with Target Speech Extraction
TSE uniquely requires both speaker identification and speech separation, distinguishing it from other tasks in the Speech processing Universal PERformance Benchmark (SUPERB) evaluation.
Target Speech Extraction with Pre-trained Self-supervised Learning Models
We then extend a powerful TSE architecture by incorporating two SSL-based modules: an Adaptive Input Enhancer (AIE) and a speaker encoder.
Array Geometry-Robust Attention-Based Neural Beamformer for Moving Speakers
Recently, a mask-based beamformer with attention-based spatial covariance matrix aggregator (ASA) was proposed, which was demonstrated to track moving sources accurately.
Lattice Rescoring Based on Large Ensemble of Complementary Neural Language Models
We investigate the effectiveness of using a large ensemble of advanced neural language models (NLMs) for lattice rescoring on automatic speech recognition (ASR) hypotheses.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Neural network-based virtual microphone estimation with virtual microphone and beamformer-level multi-task loss
However, this training objective may not be optimal for a specific array processing back-end, such as beamforming.
How does end-to-end speech recognition training impact speech enhancement artifacts?
Jointly training a speech enhancement (SE) front-end and an automatic speech recognition (ASR) back-end has been investigated as a way to mitigate the influence of \emph{processing distortion} generated by single-channel SE on ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Modified Parametric Multichannel Wiener Filter \\for Low-latency Enhancement of Speech Mixtures with Unknown Number of Speakers
This paper introduces a novel low-latency online beamforming (BF) algorithm, named Modified Parametric Multichannel Wiener Filter (Mod-PMWF), for enhancing speech mixtures with unknown and varying number of speakers.
Multi-Stream Extension of Variational Bayesian HMM Clustering (MS-VBx) for Combined End-to-End and Vector Clustering-based Diarization
Combining end-to-end neural speaker diarization (EEND) with vector clustering (VC), known as EEND-VC, has gained interest for leveraging the strengths of both methods.
ConceptBeam: Concept Driven Target Speech Extraction
We use it to bridge modality-dependent information, i. e., the speech segments in the mixture, and the specified, modality-independent concept.
Mask-based Neural Beamforming for Moving Speakers with Self-Attention-based Tracking
We thus introduce a learning-based framework that computes optimal attention weights for beamforming.
SoundBeam: Target sound extraction conditioned on sound-class labels and enrollment clues for increased performance and continuous learning
We can achieve this with a neural network that extracts the target SEs by conditioning it on clues representing the target SE classes.
How Bad Are Artifacts?: Analyzing the Impact of Speech Enhancement Errors on ASR
The artifact component is defined as the SE error signal that cannot be represented as a linear combination of speech and noise sources.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Switching Independent Vector Analysis and Its Extension to Blind and Spatially Guided Convolutional Beamforming Algorithms
This paper develops a framework that can perform denoising, dereverberation, and source separation accurately by using a relatively small number of microphones.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Blind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation
This paper proposes an approach for optimizing a Convolutional BeamFormer (CBF) that can jointly perform denoising (DN), dereverberation (DR), and source separation (SS).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Few-shot learning of new sound classes for target sound extraction
Target sound extraction consists of extracting the sound of a target acoustic event (AE) class from a mixture of AE sounds.
PILOT: Introducing Transformers for Probabilistic Sound Event Localization
Sound event localization aims at estimating the positions of sound sources in the environment with respect to an acoustic receiver (e. g. a microphone array).
Comparison of remote experiments using crowdsourcing and laboratory experiments on speech intelligibility
Many subjective experiments have been performed to develop objective speech intelligibility measures, but the novel coronavirus outbreak has made it very difficult to conduct experiments in a laboratory.
Exploiting Attention-based Sequence-to-Sequence Architectures for Sound Event Localization
Herein, attentions allow for capturing temporal dependencies in the audio signal by focusing on specific frames that are relevant for estimating the activity and direction-of-arrival of sound events at the current time-step.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Data Fusion for Audiovisual Speaker Localization: Extending Dynamic Stream Weights to the Spatial Domain
Estimating the positions of multiple speakers can be helpful for tasks like automatic speech recognition or speaker diarization.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Multimodal Attention Fusion for Target Speaker Extraction
Recently an audio-visual target speaker extraction has been proposed that extracts target speech by using complementary audio and visual clues.
Neural Network-based Virtual Microphone Estimator
Developing microphone array technologies for a small number of microphones is important due to the constraints of many devices.
Block Coordinate Descent Algorithms for Auxiliary-Function-Based Independent Vector Extraction
We also newly develop a BCD for a semiblind IVE in which the transfer functions for several super-Gaussian sources are given a priori.
Listen to What You Want: Neural Network-based Universal Sound Selector
In this paper, we propose instead a universal sound selection neural network that enables to directly select AE sounds from a mixture given user-specified target AE classes.
Tackling real noisy reverberant meetings with all-neural source separation, counting, and diarization system
Automatic meeting analysis is an essential fundamental technology required to let, e. g. smart devices follow and respond to our conversations.
Improving speaker discrimination of target speech extraction with time-domain SpeakerBeam
First, we propose a time-domain implementation of SpeakerBeam similar to that proposed for a time-domain audio separation network (TasNet), which has achieved state-of-the-art performance for speech separation.
All-neural online source separation, counting, and diarization for meeting analysis
While significant progress has been made on individual tasks, this paper presents for the first time an all-neural approach to simultaneous speaker counting, diarization and source separation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
