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Found 9 papers, 0 papers with code
TS-SEP: Joint Diarization and Separation Conditioned on Estimated Speaker Embeddings
Since diarization and source separation of meeting data are closely related tasks, we here propose an approach to perform the two objectives jointly.
Ranked #1 on
Speech Recognition
on LibriCSS
(using extra training data)
Tackling the Cocktail Fork Problem for Separation and Transcription of Real-World Soundtracks
In this paper, we focus on the cocktail fork problem, which takes a three-pronged approach to source separation by separating an audio mixture such as a movie soundtrack or podcast into the three broad categories of speech, music, and sound effects (SFX - understood to include ambient noise and natural sound events).
Reverberation as Supervision for Speech Separation
This paper proposes reverberation as supervision (RAS), a novel unsupervised loss function for single-channel reverberant speech separation.
An Exploration of Self-Supervised Pretrained Representations for End-to-End Speech Recognition
We select several pretrained speech representations and present the experimental results on various open-source and publicly available corpora for E2E-ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Deep Learning based Multi-Source Localization with Source Splitting and its Effectiveness in Multi-Talker Speech Recognition
In addition to using the prediction error as a metric for evaluating our localization model, we also establish its potency as a frontend with automatic speech recognition (ASR) as the downstream task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Directional ASR: A New Paradigm for E2E Multi-Speaker Speech Recognition with Source Localization
The advantages of D-ASR over existing methods are threefold: (1) it provides explicit speaker locations, (2) it improves the explainability factor, and (3) it achieves better ASR performance as the process is more streamlined.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings
Following the success of the 1st, 2nd, 3rd, 4th and 5th CHiME challenges we organize the 6th CHiME Speech Separation and Recognition Challenge (CHiME-6).
An Investigation of End-to-End Multichannel Speech Recognition for Reverberant and Mismatch Conditions
This report investigates the ability of E2E ASR from standard close-talk to far-field applications by encompassing entire multichannel speech enhancement and ASR components within the S2S model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Building state-of-the-art distant speech recognition using the CHiME-4 challenge with a setup of speech enhancement baseline
This paper describes a new baseline system for automatic speech recognition (ASR) in the CHiME-4 challenge to promote the development of noisy ASR in speech processing communities by providing 1) state-of-the-art system with a simplified single system comparable to the complicated top systems in the challenge, 2) publicly available and reproducible recipe through the main repository in the Kaldi speech recognition toolkit.
Ranked #2 on
Noisy Speech Recognition
on CHiME real
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
