19 citations · 49 across the 31 of their papers we have counts for
8 papers · 1 filter
Sequence-level self-learning with multiple hypotheses
Kenichi Kumatani, Dimitrios Dimitriadis, Yashesh Gaur +4
In this work, we develop new self-learning techniques with an attention-based sequence-to-sequence (seq2seq) model for automatic speech recognition (ASR). For untranscribed speech…
Separating Long-Form Speech with Group-Wise Permutation Invariant Training
Wangyou Zhang, Zhuo Chen, Naoyuki Kanda +8
Multi-talker conversational speech processing has drawn many interests for various applications such as meeting transcription. Speech separation is often required to handle overlap…
One model to enhance them all: array geometry agnostic multi-channel personalized speech enhancement
Hassan Taherian, Sefik Emre Eskimez, Takuya Yoshioka +3
With the recent surge of video conferencing tools usage, providing high-quality speech signals and accurate captions have become essential to conduct day-to-day business or connect…
Personalized Speech Enhancement: New Models and Comprehensive Evaluation
Sefik Emre Eskimez, Takuya Yoshioka, Huaming Wang +3
Personalized speech enhancement (PSE) models utilize additional cues, such as speaker embeddings like d-vectors, to remove background noise and interfering speech in real-time and…
All-neural beamformer for continuous speech separation
Zhuohuang Zhang, Takuya Yoshioka, Naoyuki Kanda +4
Continuous speech separation (CSS) aims to separate overlapping voices from a continuous influx of conversational audio containing an unknown number of utterances spoken by an unkn…
Dynamic Gradient Aggregation for Federated Domain Adaptation
Dimitrios Dimitriadis, Kenichi Kumatani, Robert Gmyr +2
In this paper, a new learning algorithm for Federated Learning (FL) is introduced. The proposed scheme is based on a weighted gradient aggregation using two-step optimization to of…
Human Listening and Live Captioning: Multi-Task Training for Speech Enhancement
Sefik Emre Eskimez, Xiaofei Wang, Min Tang +5
With the surge of online meetings, it has become more critical than ever to provide high-quality speech audio and live captioning under various noise conditions. However, most mona…
Generating Human Readable Transcript for Automatic Speech Recognition with Pre-trained Language Model
Junwei Liao, Yu Shi, Ming Gong +5
Modern Automatic Speech Recognition (ASR) systems can achieve high performance in terms of recognition accuracy. However, a perfectly accurate transcript still can be challenging t…