Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

Research on speech processing has traditionally considered the task of designing hand-
engineered acoustic features (feature engineering) as a separate distinct problem from the …

Recently great strides have been made in the field of automatic speech recognition (ASR) by
using various deep learning techniques. In this study, we present a thorough comparison …

In the last decade of automatic speech recognition (ASR) research, the introduction of deep
learning has brought considerable reductions in word error rate of more than 50% relative …

In this paper, we propose an open source, production first, and production ready speech
recognition toolkit called WeNet in which a new two-pass approach is implemented to unify …

What does it take to create the Babel Fish, a tool that can help individuals translate speech
between any two languages? While recent breakthroughs in text-based models have …

Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art
conventional models with respect to both quality, ie, word error rate (WER), and latency, ie …

Recently, we made available WeNet, a production-oriented end-to-end speech recognition
toolkit, which introduces a unified two-pass (U2) framework and a built-in runtime to address …

End-to-end (E2E) models have shown to outperform state-of-the-art conventional models for
streaming speech recognition [1] across many dimensions, including quality (as measured …

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 methods …