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Found 30 papers, 8 papers with code
Spoofing Attacker Also Benefits from Self-Supervised Pretrained Model
Large-scale pretrained models using self-supervised learning have reportedly improved the performance of speech anti-spoofing.
Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization
This paper focuses on speaker diarization and proposes to conduct the above bi-directional knowledge transfer alternately.
Updating Only Encoders Prevents Catastrophic Forgetting of End-to-End ASR Models
In this paper, we present an incremental domain adaptation technique to prevent catastrophic forgetting for an end-to-end automatic speech recognition (ASR) model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors
Finally, to improve online diarization, our method improves the buffer update method and revisits the variable chunk-size training of EEND.
Rethinking Fano's Inequality in Ensemble Learning
We propose a fundamental theory on ensemble learning that answers the central question: what factors make an ensemble system good or bad?
Improving the Naturalness of Simulated Conversations for End-to-End Neural Diarization
Due to the lack of any annotated real conversational dataset, EEND is usually pretrained on a large-scale simulated conversational dataset first and then adapted to the target real dataset.
Environmental Sound Extraction Using Onomatopoeic Words
An onomatopoeic word, which is a character sequence that phonetically imitates a sound, is effective in expressing characteristics of sound such as duration, pitch, and timbre.
Multi-Channel End-to-End Neural Diarization with Distributed Microphones
With simulated and real-recorded datasets, we demonstrated that the proposed method outperformed conventional EEND when a multi-channel input was given while maintaining comparable performance with a single-channel input.
Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors
This makes it possible to produce diarization results of a large number of speakers for the whole recording even if the number of output speakers for each subsequence is limited.
Encoder-Decoder Based Attractors for End-to-End Neural Diarization
Diarization results are then estimated as dot products of the attractors and embeddings.
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).
The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap
This paper provides a detailed description of the Hitachi-JHU system that was submitted to the Third DIHARD Speech Diarization Challenge.
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.
Hitachi at SemEval-2020 Task 8: Simple but Effective Modality Ensemble for Meme Emotion Recognition
Users of social networking services often share their emotions via multi-modal content, usually images paired with text embedded in them.
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.
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).
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.
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)
+3
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
Personalized Classifier for Food Image Recognition
In this paper, we address the personalization problem, which involves adapting to the user's domain incrementally using a very limited number of samples.
Significance of Softmax-based Features in Comparison to Distance Metric Learning-based Features
However, in these DML studies, there were no equitable comparisons between features extracted from a DML-based network and those from a softmax-based network.
