Editing State-of-the-art (section)

==== Zarazaga, P. P., Henter, G. E., & Malisz, Z. (2023, June). A processing framework to access large quantities of whispered speech found in ASMR. In ''ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)'' (pp. 1-5). IEEE. ====

* '''Summary''': This paper introduces a novel processing framework to harness large volumes of high-quality whispered speech from ASMR content. By employing an advanced whispered activity detection (WAD) system and integrating human-in-the-loop through Edyson, a bulk audio-annotation tool, the framework efficiently labels and extracts clean whispered speech segments. The approach not only aids in the development of whisper-capable speech technology but also contributes valuable linguistic data for research.
* '''RQ''': The research question addressed by the paper is how to effectively process and extract large amounts of clean whispered speech from ASMR recordings, which include a variety of background noises and non-whispered acoustic triggers.
* '''Hypothesis''': The hypothesis posited in the paper is that by leveraging sophisticated WAD techniques, coupled with human-in-the-loop annotation and data augmentation, it is possible to efficiently identify and isolate high-quality whispered speech segments from the complex acoustic landscape of ASMR content.
* '''Conclusion''': The framework presented successfully processes ASMR recordings to access and extract significant amounts of clean whispered speech, outperforming traditional methods. This success opens up new avenues for speech technology development and linguistic research, particularly in fields requiring large datasets of natural whispered speech.
* '''Critical observations''':
** The paper highlights the scarcity of whispered speech datasets and the challenges in processing ASMR content due to its diverse acoustic triggers.
** The use of deep learning for whispered activity detection significantly improves the accuracy of identifying whispered segments within noisy environments.
** Incorporating human judgment through Edyson for audio labeling enhances the precision of the extracted data, making the process more efficient and scalable.
* '''Relevance''': The research is highly relevant to advancing speech recognition technologies, especially for applications requiring whispered input. It also provides a substantial resource for studying the linguistic and acoustic properties of whispered speech, potentially impacting areas like human-computer interaction, where natural and nuanced speech inputs are increasingly important.