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Found 14 papers, 6 papers with code
Meeting Recognition with Continuous Speech Separation and Transcription-Supported Diarization
We propose a modular pipeline for the single-channel separation, recognition, and diarization of meeting-style recordings and evaluate it on the Libri-CSS dataset.
Mixture Encoder Supporting Continuous Speech Separation for Meeting Recognition
This mixture encoder leverages the original overlapped speech to mitigate the effect of artifacts introduced by the speech separation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
MeetEval: A Toolkit for Computation of Word Error Rates for Meeting Transcription Systems
MeetEval is an open-source toolkit to evaluate all kinds of meeting transcription systems.
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).
MMS-MSG: A Multi-purpose Multi-Speaker Mixture Signal Generator
Training and evaluation of these single tasks requires synthetic data with access to intermediate signals that is as close as possible to the evaluation scenario.
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.
A Meeting Transcription System for an Ad-Hoc Acoustic Sensor Network
We propose a system that transcribes the conversation of a typical meeting scenario that is captured by a set of initially unsynchronized microphone arrays at unknown positions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Monaural source separation: From anechoic to reverberant environments
Impressive progress in neural network-based single-channel speech source separation has been made in recent years.
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.
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.
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
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.
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
