Search Results for author: Minho Jin

Found 4 papers, 1 papers with code

openFEAT: Improving Speaker Identification by Open-set Few-shot Embedding Adaptation with Transformer

no code implementations24 Feb 2022 Kishan K C, Zhenning Tan, Long Chen, Minho Jin, Eunjung Han, Andreas Stolcke, Chul Lee

Household speaker identification with few enrollment utterances is an important yet challenging problem, especially when household members share similar voice characteristics and room acoustics.

Open Set Learning Speaker Identification

Contrastive-mixup learning for improved speaker verification

no code implementations22 Feb 2022 Xin Zhang, Minho Jin, Roger Cheng, Ruirui Li, Eunjung Han, Andreas Stolcke

In this work, we propose contrastive-mixup, a novel augmentation strategy that learns distinguishing representations based on a distance metric.

Data Augmentation Metric Learning +1

A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency

no code implementations28 Mar 2020 Tara N. Sainath, Yanzhang He, Bo Li, Arun Narayanan, Ruoming Pang, Antoine Bruguier, Shuo-Yiin Chang, Wei Li, Raziel Alvarez, Zhifeng Chen, Chung-Cheng Chiu, David Garcia, Alex Gruenstein, Ke Hu, Minho Jin, Anjuli Kannan, Qiao Liang, Ian McGraw, Cal Peyser, Rohit Prabhavalkar, Golan Pundak, David Rybach, Yuan Shangguan, Yash Sheth, Trevor Strohman, Mirko Visontai, Yonghui Wu, Yu Zhang, Ding Zhao

Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i. e., word error rate (WER), and latency, i. e., the time the hypothesis is finalized after the user stops speaking.

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