Mobile and embedded devices are increasingly using microphones and audio-based
computational models to infer user context. A major challenge in building systems that …

The goal of acoustic matching is to transform an audio recording made in one acoustic
environment to sound as if it had been recorded in a different environment, based on …

In order to address the commonly met issue of overfitting in speech recognition, this article
investigates Multi-Task Learning, when the auxiliary task focuses on speaker classification …

In this work, we investigate the use of embeddings for speaker-adaptive training of DNNs
(DNN-SAT) focusing on a small amount of adaptation data per speaker. DNN-SAT can be …

In order to properly train an automatic speech recognition system, speech with its annotated
transcriptions is most often required. The amount of real annotated data recorded in noisy …

The emergence of pandemics poses a persistent threat to both global health and economic
stability. While zoonotic spillovers and local outbreaks may not be fully preventable, early …

In this paper, we focus on the task of small-footprint keyword spotting under the far-field
scenario. Far-field environments are commonly encountered in real-life speech applications …

Wake-up word detection models are widely used in real life, but suffer from severe
performance degradation when encountering adversarial samples. In this paper we discuss …

This paper presents a novel deep neural network architecture for transfer learning in
acoustic models. A well-known approach for transfer leaning is using target domain data to …

Deep Learning technologies are becoming the major approaches for natural signal and
information processings, including for speech recognition. Many architectures for deep …