Editing State-of-the-art (section)

==== 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.