This paper empirically investigates the influence of different data splits and splitting
strategies on the performance of dysfluency detection systems. For this, we perform …

In multi-lingual societies, where multiple languages are spoken in a small geographic
vicinity, informal conversations often involve mix of languages. Existing speech technologies …

Abstract Conventionally, Automatic Speech Recognition (ASR) systems are evaluated on
their ability to correctly recognize each word contained in a speech signal. In this context, the …

Numerous automatic speech recognition (ASR) models have been developed in recent
years, but they suffer from the drawback of being large models that take more time to train …

The evaluation of automatic speech transcriptions relies heavily on metrics such as Word
Error Rate (WER) and Character Error Rate (CER). However, these metrics have faced …

As the proliferation of video content continues, and many video archives lack suitable
metadata, therefore, video retrieval, particularly through example‐based search, has …

Mobile devices are integral to daily life, necessitating secure authentication methods like
speaker verification for enhanced security and convenience. While deep neural networks …

Speech intelligibility (SI) prediction models are a valuable tool for the development of
speech processing algorithms for hearing aids or consumer electronics. For the use in …

Voice disorders like dysphonia can significantly impact a person's quality of life, so proper
diagnostic methods are crucial. Previous approaches have primarily used datasets of a …

Currently, deep speaker embedding models are the most advanced feature extraction
methods for speaker verification. However, their effectiveness in identifying children's voices …