DARF: A data-reduced FADE version for simulations of speech recognition thresholds with real hearing aids

ASR-based speech intelligibility prediction: A review

Various types of methods and approaches are available to predict the intelligibility of speech signals, but many of these still suffer from two major problems: first, their required prior knowledge, which itself could limit the applicability and lower the objectivity of the method, and second, a low generalization capacity, e.g. across noise types, degradation conditions, and speech material. Automatic speech recognition (ASR) has been suggested as a machine-learning-based component of speech intelligibility prediction (SIP), aiming to ameliorate the shortcomings of other SIP methods. Since their first introduction, ASR-based SIP approaches have been developing at an increasingly rapid pace, were deployed in a range of contexts, and have shown promising performance in many scenarios. Our article provides an overview of this body of research. The main differences between competing methods are highlighted and their benefits are explained next to their limitations. We conclude with an outlook on future work and new related directions.

Microscopic and Blind Prediction of Speech Intelligibility: Theory and Practice

Being able to estimate speech intelligibility without the need for listening tests would confer great benefits for a wide range of speech processing applications. Many attempts have therefore been made to introduce an objective, and ideally referencefree measure for this purpose. Most works analyze speech intelligibility prediction (SIP) methods from a macroscopic point of view, averaging over longer time spans. This paper, in contrast, presents a theoretical framework for the microscopic evaluation of SIP methods. Within our framework, a Statistically estimated Accuracy based on Theory (StAT) is derived, which numerically quantifies the statistical limitations inherent in microscopic SIP. A state-of-the-art approach to microscopic SIP, namely, the use of automatic speech recognition (ASR) to directly predict listening test results, is evaluated within this framework. The practical results are in good agreement with the theory. As the final contribution, a fully blind DIscriminative Speech intelligibility Predictor (DISP) is introduced and is also evaluated within the StAT framework. It is shown that this novel, blind estimator can predict intelligibility as well as-and often even with better accuracy than-the non-blind ASR-based approach, and that its results are again in good agreement with its theoretically derived performance potential.

Analysis Model of Spoken English Evaluation Algorithm Based on Intelligent Algorithm of Internet of Things

With the in-depth promotion of the national strategy for the integration of artificial intelligence technology and entity development, speech recognition processing technology, as an important medium of human-computer interaction, has received extensive attention and motivated research in industry and academia. However, the existing accurate speech recognition products are based on massive data platform, which has the problems of slow response and security risk, which makes it difficult for the existing speech recognition products to meet the application requirements for timely translation of speech with high response time and network security requirements under the condition of network instability and insecurity. Based on this, this paper studies the analysis model of oral English evaluation algorithm based on Internet of things intelligent algorithm in speech recognition technology. Firstly, based on the automatic machine learning and lightweight learning strategy, a lightweight technology of automatic speech recognition depth neural network adapted to the edge computing power is proposed. Secondly, the quantitative evaluation of Internet of things intelligent classification algorithm and big data analysis in this system is described. In the evaluation, the evaluation method of oral English characteristics is adopted. At the same time, the Internet of things intelligent classification algorithm and big data analysis strategy are used to evaluate the accuracy of oral English. Finally, the experimental results show that the oral English feature recognition system based on Internet of things intelligent classification algorithm and big data analysis has the advantages of good reliability, high intelligence, and strong ability to resist subjective factors, which proves the advantages of Internet of things intelligent classification algorithm and big data analysis in English feature recognition.