The polarization of world languages is becoming more and more obvious. Many languages,
mainly endangered languages, are of low-resource attribute due to lack of information. Both …

Off-the-shelf pre-trained Automatic Speech Recognition (ASR) systems are an increasingly
viable service for companies of any size building speech-based products. While these ASR …

This paper describes deep learning approaches for the Mask and Breathing Sub-
Challenges (SCs), which are addressed by the INTERSPEECH 2020 Computational …

Abstract Domain Adaptation for Automatic Speech Recognition (ASR) error correction via
machine translation is a useful technique for improving out-of-domain outputs of pre-trained …

Previous work on end-to-end translation from speech has primarily used frame-level
features as speech representations, which creates longer, sparser sequences than text. We …

The aim of conversational speech processing is to analyze human conversations in natural
settings. It finds numerous applications in personality traits identification, speech therapy …

Multilingual acoustic models have been successfully applied to low-resource speech
recognition. Most existing works have combined many small corpora together and …

In this paper, we study an application of transfer learning approach to speaker's age and
gender recognition task. Recently, speech analysis systems, which take images of log Mel …

COMPUTATIONAL TOOLS FOR ENDANGERED LANGUAGE DOCUMENTATION A
Dissertation Submitted to the Graduate School of the University of N Page 1 …

Code-Switching (CS) remains a challenge for Automatic Speech Recognition (ASR),
especially character-based models. With the combined choice of characters from multiple …