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19942025
most citedA Comparative Study on Transformer vs RNN in Speech Applications

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eess.AS2023★ 29 cited

TS-SEP: Joint Diarization and Separation Conditioned on Estimated Speaker Embeddings

Christoph Boeddeker, Aswin Shanmugam Subramanian, Gordon Wichern +2

Since diarization and source separation of meeting data are closely related tasks, we here propose an approach to perform the two objectives jointly. It builds upon the target-spea…

eess.AS2021

Dual Causal/Non-Causal Self-Attention for Streaming End-to-End Speech Recognition

Niko Moritz, Takaaki Hori, Jonathan Le Roux

Attention-based end-to-end automatic speech recognition (ASR) systems have recently demonstrated state-of-the-art results for numerous tasks. However, the application of self-atten…

eess.AS2020★ 4 cited

Unsupervised Speaker Adaptation using Attention-based Speaker Memory for End-to-End ASR

Leda Sarı, Niko Moritz, Takaaki Hori +1

We propose an unsupervised speaker adaptation method inspired by the neural Turing machine for end-to-end (E2E) automatic speech recognition (ASR). The proposed model contains a me…

eess.AS2020★ 5 cited

End-to-End Multi-speaker Speech Recognition with Transformer

Xuankai Chang, Wangyou Zhang, Yanmin Qian +2

Recently, fully recurrent neural network (RNN) based end-to-end models have been proven to be effective for multi-speaker speech recognition in both the single-channel and multi-ch…

eess.AS2018★ 2 cited

Promising Accurate Prefix Boosting for sequence-to-sequence ASR

Murali Karthick Baskar, Lukáš Burget, Shinji Watanabe +3

In this paper, we present promising accurate prefix boosting (PAPB), a discriminative training technique for attention based sequence-to-sequence (seq2seq) ASR. PAPB is devised to…

eess.AS2018★ 2 cited

CNN-based MultiChannel End-to-End Speech Recognition for everyday home environments

Nelson Yalta, Shinji Watanabe, Takaaki Hori +2

Casual conversations involving multiple speakers and noises from surrounding devices are common in everyday environments, which degrades the performances of automatic speech recogn…