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Found 17 papers, 6 papers with code
Team Hitachi @ AutoMin 2021: Reference-free Automatic Minuting Pipeline with Argument Structure Construction over Topic-based Summarization
This paper introduces the proposed automatic minuting system of the Hitachi team for the First Shared Task on Automatic Minuting (AutoMin-2021).
Semi-Supervised Training with Pseudo-Labeling for End-to-End Neural Diarization
To evaluate our proposed method, we conduct the experiments of model adaptation using labeled and unlabeled data.
End-to-End Speaker Diarization Conditioned on Speech Activity and Overlap Detection
In this paper, we present a conditional multitask learning method for end-to-end neural speaker diarization (EEND).
Online Streaming End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers
We propose a streaming diarization method based on an end-to-end neural diarization (EEND) model, which handles flexible numbers of speakers and overlapping speech.
Speaker Diarization
Sound
Audio and Speech Processing
End-to-End Speaker Diarization as Post-Processing
Clustering-based diarization methods partition frames into clusters of the number of speakers; thus, they typically cannot handle overlapping speech because each frame is assigned to one speaker.
Block-Online Guided Source Separation
It is also a problem that the offline GSS is an utterance-wise algorithm so that it produces latency according to the length of the utterance.
Utterance-Wise Meeting Transcription System Using Asynchronous Distributed Microphones
We also showed that our framework achieved CER of 21. 8 %, which is only 2. 1 percentage points higher than the CER in headset microphone-based transcription.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Online End-to-End Neural Diarization with Speaker-Tracing Buffer
This paper proposes a novel online speaker diarization algorithm based on a fully supervised self-attention mechanism (SA-EEND).
Neural Speaker Diarization with Speaker-Wise Chain Rule
Speaker diarization is an essential step for processing multi-speaker audio.
End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors
End-to-end speaker diarization for an unknown number of speakers is addressed in this paper.
End-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-label Classification
However, the clustering-based approach has a number of problems; i. e., (i) it is not optimized to minimize diarization errors directly, (ii) it cannot handle speaker overlaps correctly, and (iii) it has trouble adapting their speaker embedding models to real audio recordings with speaker overlaps.
Addressing Ambiguity of Emotion Labels Through Meta-Learning
We therefore propose a dynamic label correction and sample contribution weight estimation model.
Simultaneous Speech Recognition and Speaker Diarization for Monaural Dialogue Recordings with Target-Speaker Acoustic Models
Our proposed method combined with i-vector speaker embeddings ultimately achieved a WER that differed by only 2. 1 % from that of TS-ASR given oracle speaker embeddings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
End-to-End Neural Speaker Diarization with Self-attention
Our method was even better than that of the state-of-the-art x-vector clustering-based method.
Ranked #2 on
Speaker Diarization
on CALLHOME
End-to-End Neural Speaker Diarization with Permutation-Free Objectives
To realize such a model, we formulate the speaker diarization problem as a multi-label classification problem, and introduces a permutation-free objective function to directly minimize diarization errors without being suffered from the speaker-label permutation problem.
Ranked #6 on
Speaker Diarization
on CALLHOME
Auxiliary Interference Speaker Loss for Target-Speaker Speech Recognition
In this paper, we propose a novel auxiliary loss function for target-speaker automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University Joint Investigation for Dinner Party ASR
In this paper, we present Hitachi and Paderborn University's joint effort for automatic speech recognition (ASR) in a dinner party scenario.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
