Search Results for author:
Found 35 papers, 10 papers with code
Neural Target Speech Extraction: An Overview
Humans can listen to a target speaker even in challenging acoustic conditions that have noise, reverberation, and interfering speakers.
On Word Error Rate Definitions and their Efficient Computation for Multi-Speaker Speech Recognition Systems
We propose a general framework to compute the word error rate (WER) of ASR systems that process recordings containing multiple speakers at their input and that produce multiple output word sequences (MIMO).
Utterance-by-utterance overlap-aware neural diarization with Graph-PIT
In this paper, we argue that such an approach involving the segmentation has several issues; for example, it inevitably faces a dilemma that larger segment sizes increase both the context available for enhancing the performance and the number of speakers for the local EEND module to handle.
Strategies to Improve Robustness of Target Speech Extraction to Enrollment Variations
Experimental validation reveals the effectiveness of both worst-enrollment target training and SI-loss training to improve robustness against enrollment variations, by increasing speaker discriminability.
Listen only to me! How well can target speech extraction handle false alarms?
Target speech extraction (TSE) extracts the speech of a target speaker in a mixture given auxiliary clues characterizing the speaker, such as an enrollment utterance.
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.
Tight integration of neural- and clustering-based diarization through deep unfolding of infinite Gaussian mixture model
Speaker diarization has been investigated extensively as an important central task for meeting analysis.
Learning to Enhance or Not: Neural Network-Based Switching of Enhanced and Observed Signals for Overlapping Speech Recognition
To mitigate the degradation, we introduced a rule-based method to switch the ASR input between the enhanced and observed signals, which showed promising results.
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
SA-SDR: A novel loss function for separation of meeting style data
Many state-of-the-art neural network-based source separation systems use the averaged Signal-to-Distortion Ratio (SDR) as a training objective function.
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
Graph-PIT: Generalized permutation invariant training for continuous separation of arbitrary numbers of speakers
When processing meeting-like data in a segment-wise manner, i. e., by separating overlapping segments independently and stitching adjacent segments to continuous output streams, this constraint has to be fulfilled for any segment.
Speeding Up Permutation Invariant Training for Source Separation
The Hungarian algorithm can be used for uPIT and we introduce various algorithms for the Graph-PIT assignment problem to reduce the complexity to be polynomial in the number of utterances.
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).
Should We Always Separate?: Switching Between Enhanced and Observed Signals for Overlapping Speech Recognition
', we analyze ASR performance on observed and enhanced speech at various noise and interference conditions, and show that speech enhancement degrades ASR under some conditions even for overlapping speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Advances in integration of end-to-end neural and clustering-based diarization for real conversational speech
This paper is to (1) report recent advances we made to this framework, including newly introduced robust constrained clustering algorithms, and (2) experimentally show that the method can now significantly outperform competitive diarization methods such as Encoder-Decoder Attractor (EDA)-EEND, on CALLHOME data which comprises real conversational speech data including overlapped speech and an arbitrary number of speakers.
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
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.
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
End-to-End Dereverberation, Beamforming, and Speech Recognition with Improved Numerical Stability and Advanced Frontend
Recently, the end-to-end approach has been successfully applied to multi-speaker speech separation and recognition in both single-channel and multichannel conditions.
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.
Speaker activity driven neural speech extraction
Target speech extraction, which extracts the speech of a target speaker in a mixture given auxiliary speaker clues, has recently received increased interest.
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.
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.
Integrating end-to-end neural and clustering-based diarization: Getting the best of both worlds
In this paper, we propose a simple but effective hybrid diarization framework that works with overlapped speech and for long recordings containing an arbitrary number of speakers.
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.
Multi-talker ASR for an unknown number of sources: Joint training of source counting, separation and ASR
Most approaches to multi-talker overlapped speech separation and recognition assume that the number of simultaneously active speakers is given, but in realistic situations, it is typically unknown.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Improving noise robust automatic speech recognition with single-channel time-domain enhancement network
With the advent of deep learning, research on noise-robust automatic speech recognition (ASR) has progressed rapidly.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
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.
End-to-end training of time domain audio separation and recognition
The rising interest in single-channel multi-speaker speech separation sparked development of End-to-End (E2E) approaches to multi-speaker speech recognition.
Jointly optimal dereverberation and beamforming
We previously proposed an optimal (in the maximum likelihood sense) convolutional beamformer that can perform simultaneous denoising and dereverberation, and showed its superiority over the widely used cascade of a WPE dereverberation filter and a conventional MPDR beamformer.
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
