Furthermore, a unique technique is proposed that involves mixing the input audio with additional audio, and using the additional audio as a reference.
The experiments with the two-speaker CALLHOME dataset show that the intermediate labels with the proposed non-autoregressive intermediate attractors boost the diarization performance.
The proposed method exploits the conditioning framework of self-conditioned CTC to train robust models by conditioning with "noisy" intermediate predictions.
End-to-end automatic speech recognition directly maps input speech to characters.
Diarization results are then estimated as dot products of the attractors and embeddings.
To evaluate our proposed method, we conduct the experiments of model adaptation using labeled and unlabeled data.
In this paper, we present a conditional multitask learning method for end-to-end neural speaker diarization (EEND).
This paper provides a detailed description of the Hitachi-JHU system that was submitted to the Third DIHARD Speech Diarization Challenge.
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
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.
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.
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.
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
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 #2 on Speech Separation on WSJ0-5mix
This paper proposes a novel online speaker diarization algorithm based on a fully supervised self-attention mechanism (SA-EEND).
Speaker diarization is an essential step for processing multi-speaker audio.
End-to-end speaker diarization for an unknown number of speakers is addressed in this paper.
no code implementations • 20 Apr 2020 • Shinji Watanabe, Michael Mandel, Jon Barker, Emmanuel Vincent, Ashish Arora, Xuankai Chang, Sanjeev Khudanpur, Vimal Manohar, Daniel Povey, Desh Raj, David Snyder, Aswin Shanmugam Subramanian, Jan Trmal, Bar Ben Yair, Christoph Boeddeker, Zhaoheng Ni, Yusuke Fujita, Shota Horiguchi, Naoyuki Kanda, Takuya Yoshioka, Neville Ryant
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).
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 is an important pre-processing step for many speech applications, and it aims to solve the "who spoke when" problem.
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.
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
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
In this paper, we propose a novel auxiliary loss function for target-speaker automatic speech recognition (ASR).
In this paper, we present Hitachi and Paderborn University's joint effort for automatic speech recognition (ASR) in a dinner party scenario.