This paper investigates data augmentation for deep neural network acoustic modeling
based on label-preserving transformations to deal with data sparsity. Two data …

Self-supervised speech representation learning (speech SSL) has demonstrated the benefit
of scale in learning rich representations for Automatic Speech Recognition (ASR) with …

With the development of continuous speech recognition technology, users have put forward
higher requirements in terms of speech recognition accuracy. Low-resource speech …

Deep neural networks (DNNs) have achieved great success in acoustic modeling for speech
recognition. However, DNNs with sigmoid neurons may suffer from the vanishing gradient …

Spoken content retrieval refers to directly indexing and retrieving spoken content based on
the audio rather than text descriptions. This potentially eliminates the requirement of …

We explore techniques to improve the robustness of small-footprint keyword spotting models
based on deep neural networks (DNNs) in the presence of background noise and in far-field …

As a feed-forward architecture, the recently proposed maxout networks integrate dropout
naturally and show state-of-the-art results on various computer vision datasets. This paper …

Automatic emotion detection is a very attractive field of research that can help build more
natural human–machine interaction systems. However, several issues arise when real …

The Listen, Attend and Spell (LAS) model and other attention-based automatic speech
recognition (ASR) models have known limitations when operated in a fully online mode. In …

We present a system for keyword search on Cantonese conversational telephony audio,
collected for the IARPA Babel program, that achieves good performance by combining …