416 citations · 662 across the 9 of their papers we have counts for
23 papers
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Shuai Lu, Daya Guo, Shuo Ren +19
Benchmark datasets have a significant impact on accelerating research in programming language tasks. In this paper, we introduce CodeXGLUE, a benchmark dataset to foster machine le…
UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data
Chengyi Wang, Yu Wu, Yao Qian +5
In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data, in which supervised phonetic CTC le…
Microsoft Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2020
Xiong Xiao, Naoyuki Kanda, Zhuo Chen +10
This paper describes the Microsoft speaker diarization system for monaural multi-talker recordings in the wild, evaluated at the diarization track of the VoxCeleb Speaker Recogniti…
CodeBLEU: a Method for Automatic Evaluation of Code Synthesis
Shuo Ren, Daya Guo, Shuai Lu +7
Evaluation metrics play a vital role in the growth of an area as it defines the standard of distinguishing between good and bad models. In the area of code synthesis, the commonly…
MoBoAligner: a Neural Alignment Model for Non-autoregressive TTS with Monotonic Boundary Search
Naihan Li, Shujie Liu, Yanqing Liu +3
To speed up the inference of neural speech synthesis, non-autoregressive models receive increasing attention recently. In non-autoregressive models, additional durations of text to…
On the Comparison of Popular End-to-End Models for Large Scale Speech Recognition
Jinyu Li, Yu Wu, Yashesh Gaur +3
Recently, there has been a strong push to transition from hybrid models to end-to-end (E2E) models for automatic speech recognition. Currently, there are three promising E2E method…