Automatic speech recognition systems generally produce unpunctuated text which is difficult
to read for humans and degrades the performance of many downstream machine …

Leveraging large-scale unlabeled web videos such as instructional videos for pre-training
followed by task-specific finetuning has become the de facto approach for many video-and …

In this paper we propose an approach to predict punctuation marks for unsegmented speech
transcript. The approach is purely lexical, with pre-trained Word Vectors as the only input. A …

This paper describes the current TTÜ speech transcription system for Estonian speech. The
system is designed to handle semi-spontaneous speech, such as broadcast conversations …

Ensuring proper punctuation and letter casing is a key pre-processing step towards applying
complex natural language processing algorithms. This is especially significant for textual …

This paper proposes to use self-attention based model to predict punctuation marks for word
sequences. The model is trained using word and speech embedding features which are …

An ASR system usually does not predict any punctuation or capitalization. Lack of
punctuation causes problems in result presentation and confuses both the human reader …

Though people rarely speak in complete sentences, punctuation confers many benefits to
the readers of transcribed speech. Unfortunately, most ASR systems do not produce …

The integrity of geomagnetic data is a critical factor in understanding the evolutionary
process of Earth's magnetic field, as it provides useful information for near-surface …

Previous studies demonstrate that word embeddings and part-of-speech (POS) tags are
helpful for punctuation restoration tasks. However, two drawbacks still exist. One is that word …