With the ever‐growing adoption of artificial intelligence (AI)‐based systems, the carbon
footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to …

This paper presents the prototype of end-to-end speech recognition, storage, and
postprocessing tasks to build speech analytics, real-time agent augmentation, and other …

Wav2Vec2. 0 is a state-of-the-art model which learns speech representations through
unlabeled speech data, aka, self supervised learning. The pretrained model is then fine …

This study is devoted to the transcription of human speech in the Kazakh language in
dynamically changing conditions. It discusses key aspects related to the phonetic structure …

Long Short-Term Memory (LSTM) finds extensive application in sequential learning tasks,
notably in speech recognition. However, existing accelerators tailored for traditional LSTM …

Recent transformer-based ASR models have achieved word-error rates (WER) below 4%,
surpassing human annotator accuracy, yet they demand extensive server resources …

The widespread integration of Machine Learning (ML) in software systems has brought forth
unprecedented advancements, yet the surge in energy consumption raises ecological …

Automated speech and pause/silence detection is a crucial task in clinical and pathological
environments, supporting diagnostic processes and providing essential information for …

As automatic speech recognition (ASR) has improved, it has become a viable tool for
content transcription. Prior to the use of ASR for this task, content transcription was achieved …

The convergence of edge computing and Artificial Intelligence, namely Edge AI, offers many
opportunities to the industry for building competitive and innovative business models …