Search Results for author:
Found 27 papers, 8 papers with code
Keep Decoding Parallel with Effective Knowledge Distillation from Language Models to End-to-end Speech Recognisers
This study presents a novel approach for knowledge distillation (KD) from a BERT teacher model to an automatic speech recognition (ASR) model using intermediate layers.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Audio Difference Learning for Audio Captioning
Furthermore, a unique technique is proposed that involves mixing the input audio with additional audio, and using the additional audio as a reference.
Neural Diarization with Non-autoregressive Intermediate Attractors
The experiments with the two-speaker CALLHOME dataset show that the intermediate labels with the proposed non-autoregressive intermediate attractors boost the diarization performance.
InterAug: Augmenting Noisy Intermediate Predictions for CTC-based ASR
The proposed method exploits the conditioning framework of self-conditioned CTC to train robust models by conditioning with "noisy" intermediate predictions.
Alternate Intermediate Conditioning with Syllable-level and Character-level Targets for Japanese ASR
End-to-end automatic speech recognition directly maps input speech to characters.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Better Intermediates Improve CTC Inference
This paper proposes a method for improved CTC inference with searched intermediates and multi-pass conditioning.
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.
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
Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals
This model additionally has a simple and efficient stop criterion for the end of the transduction, making it able to infer the variable number of output sequences.
Ranked #3 on
Speech Separation
on WSJ0-4mix
Speaker-Conditional Chain Model for Speech Separation and Extraction
With the predicted speaker information from whole observation, our model is helpful to solve the problem of conventional speech separation and speaker extraction for multi-round long recordings.
Audio and Speech Processing Sound
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.
Speaker Diarization with Region Proposal Network
Speaker diarization is an important pre-processing step for many speech applications, and it aims to solve the "who spoke when" problem.
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
