Editing State-of-the-art (section)

=== Article summaries ===

==== Kartik, A., Andrew, R., Abhinav, S., Bhu-vana, R., & Brian, K. (2017, March). End-to-end ASR-free keyword search from speech. In ''2017 IEEE International Conference on Acoustics, Speech and Signal Processing''. ====

* '''Summary''': The paper introduces an end-to-end ASR-free system for keyword search (KWS) from speech, which leverages minimal supervision. The system comprises three sub-systems: an RNN-based acoustic auto-encoder, a CNN-RNN character language model, and a feed-forward neural network for KWS. This architecture eliminates the need for conventional ASR systems and transcription of audio data, enabling faster training and performance that rivals traditional methods.
* '''RQ''': The main research question explored is whether an end-to-end ASR-free system can effectively perform text query-based keyword search from speech with minimal supervision, and how its performance compares to traditional ASR-based systems.
* '''Hypothesis''': The hypothesis posited is that an end-to-end ASR-free keyword search system, despite not utilizing a conventional ASR system or fully transcribed training audio, can still achieve respectable performance in identifying keywords within speech utterances.
* '''Conclusion''': The ASR-free E2E KWS system demonstrated the ability to perform keyword search tasks with minimal supervision, achieving respectable results compared to a conventional hybrid HMM-DNN ASR system but with significantly reduced training time. This system represents a promising direction for efficient and scalable KWS from speech without relying on comprehensive transcription data or traditional ASR systems.
* '''Critical observations''':
** The E2E system's performance on in-vocabulary (IV) and out-of-vocabulary (OOV) queries is noteworthy, especially for OOV queries where it slightly outperforms the hybrid ASR system.
** The system's performance is limited for shorter queries, indicating challenges in capturing reliable representations for queries lacking context.
** The efficiency in training time (36 times faster than traditional methods) without substantial loss in accuracy points to the potential for scalability and application in low-resource settings.
* '''Relevance''': This work has significant implications for the field of speech recognition and information retrieval, especially in environments where rapid deployment and adaptation are critical. By demonstrating that an ASR-free approach can yield comparable performance to more traditional, labor-intensive systems, this research opens up new possibilities for keyword search applications in multilingual and resource-constrained scenarios.

==== 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.