85 citations · 216 across the 21 of their papers we have counts for
20 papers · 1 filter
On Speaker Attribution with SURT
Desh Raj, Matthew Wiesner, Matthew Maciejewski +3
The Streaming Unmixing and Recognition Transducer (SURT) has recently become a popular framework for continuous, streaming, multi-talker speech recognition (ASR). With advances in…
Adapting self-supervised models to multi-talker speech recognition using speaker embeddings
Zili Huang, Desh Raj, Paola García +1
Self-supervised learning (SSL) methods which learn representations of data without explicit supervision have gained popularity in speech-processing tasks, particularly for single-t…
Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization
Shota Horiguchi, Yuki Takashima, Shinji Watanabe +1
Due to the high performance of multi-channel speech processing, we can use the outputs from a multi-channel model as teacher labels when training a single-channel model with knowle…
Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors
Shota Horiguchi, Shinji Watanabe, Paola Garcia +2
A method to perform offline and online speaker diarization for an unlimited number of speakers is described in this paper. End-to-end neural diarization (EEND) has achieved overlap…
Investigating self-supervised learning for speech enhancement and separation
Zili Huang, Shinji Watanabe, Shu-wen Yang +2
Speech enhancement and separation are two fundamental tasks for robust speech processing. Speech enhancement suppresses background noise while speech separation extracts target spe…
Multi-Channel End-to-End Neural Diarization with Distributed Microphones
Shota Horiguchi, Yuki Takashima, Paola Garcia +2
Recent progress on end-to-end neural diarization (EEND) has enabled overlap-aware speaker diarization with a single neural network. This paper proposes to enhance EEND by using mul…
Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors
Shota Horiguchi, Shinji Watanabe, Paola Garcia +3
Attractor-based end-to-end diarization is achieving comparable accuracy to the carefully tuned conventional clustering-based methods on challenging datasets. However, the main draw…
End-to-End Speaker Diarization Conditioned on Speech Activity and Overlap Detection
Yuki Takashima, Yusuke Fujita, Shinji Watanabe +3
In this paper, we present a conditional multitask learning method for end-to-end neural speaker diarization (EEND). The EEND system has shown promising performance compared with tr…
Encoder-Decoder Based Attractors for End-to-End Neural Diarization
Shota Horiguchi, Yusuke Fujita, Shinji Watanabe +2
This paper investigates an end-to-end neural diarization (EEND) method for an unknown number of speakers. In contrast to the conventional cascaded approach to speaker diarization,…
The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap
Shota Horiguchi, Nelson Yalta, Paola Garcia +7
This paper provides a detailed description of the Hitachi-JHU system that was submitted to the Third DIHARD Speech Diarization Challenge. The system outputs the ensemble results of…
End-to-End Speaker Diarization as Post-Processing
Shota Horiguchi, Paola Garcia, Yusuke Fujita +2
This paper investigates the utilization of an end-to-end diarization model as post-processing of conventional clustering-based diarization. Clustering-based diarization methods par…
DOVER-Lap: A Method for Combining Overlap-aware Diarization Outputs
Desh Raj, Leibny Paola Garcia-Perera, Zili Huang +4
Several advances have been made recently towards handling overlapping speech for speaker diarization. Since speech and natural language tasks often benefit from ensemble techniques…
Mixture of Speaker-type PLDAs for Children's Speech Diarization
Jiamin Xie, Suzanna Sia, Paola Garcia +2
In diarization, the PLDA is typically used to model an inference structure which assumes the variation in speech segments be induced by various speakers. The speaker variation is t…
The JHU Multi-Microphone Multi-Speaker ASR System for the CHiME-6 Challenge
Ashish Arora, Desh Raj, Aswin Shanmugam Subramanian +7
This paper summarizes the JHU team's efforts in tracks 1 and 2 of the CHiME-6 challenge for distant multi-microphone conversational speech diarization and recognition in everyday h…
Single Channel Far Field Feature Enhancement For Speaker Verification In The Wild
Phani Sankar Nidadavolu, Saurabh Kataria, Paola García-Perera +2
We investigated an enhancement and a domain adaptation approach to make speaker verification systems robust to perturbations of far-field speech. In the enhancement approach, using…
Speaker Diarization with Region Proposal Network
Zili Huang, Shinji Watanabe, Yusuke Fujita +4
Speaker diarization is an important pre-processing step for many speech applications, and it aims to solve the "who spoke when" problem. Although the standard diarization systems c…
Speaker detection in the wild: Lessons learned from JSALT 2019
Paola Garcia, Jesus Villalba, Herve Bredin +21
This paper presents the problems and solutions addressed at the JSALT workshop when using a single microphone for speaker detection in adverse scenarios. The main focus was to tack…
Overlap-aware diarization: resegmentation using neural end-to-end overlapped speech detection
Latané Bullock, Hervé Bredin, Leibny Paola Garcia-Perera
We address the problem of effectively handling overlapping speech in a diarization system. First, we detail a neural Long Short-Term Memory-based architecture for overlap detection…
Unsupervised Feature Enhancement for speaker verification
Phani Sankar Nidadavolu, Saurabh Kataria, Jesús Villalba +2
The task of making speaker verification systems robust to adverse scenarios remain a challenging and an active area of research. We developed an unsupervised feature enhancement ap…
End-to-end Domain-Adversarial Voice Activity Detection
Marvin Lavechin, Marie-Philippe Gill, Ruben Bousbib +2
Voice activity detection is the task of detecting speech regions in a given audio stream or recording. First, we design a neural network combining trainable filters and recurrent l…