In this study, we investigate whether noise-augmented training can concurrently improve adversarial robustness in automatic speech recognition (ASR) systems. We conduct a comparative analysis of the adversarial robustness of four different ASR architectures, each trained under three different augmentation conditions: (1) background noise, speed variations, and reverberations; (2) speed variations only; (3) no data augmentation. We then evaluate the robustness of all resulting models against attacks with white-box or black-box adversarial examples. Our results demonstrate that noise augmentation not only enhances model performance on noisy speech but also improves the model’s robustness to adversarial attacks. • Comparative analysis: We examine for state-of-the-art automatic speech recognition (ASR) architectures under three different training conditions and evaluate their robustness with respect to white- and black-box adversarial attacks. • Practical implications: Our work has direct practical applications, as it shows that noise-augmented training, which is much cheeper than adversarial training, already helps in reducing the risks for adversarial attacks. This has not been explored in the context of automatic speech recognition yet and, thus, is a relevant contribution. • Contribution to security and privacy: Our paper provides necessary and useful insights to improve robustness against adversarial attacks.

Lexical Alignment is a phenomenon often found in human–human conversations, where the interlocutors converge during a conversation to use the same terms and phrases for the same underlying concepts. Alignment (linguistic) is a mechanism used by humans for better communication between interlocutors at various levels of linguistic knowledge and features, and one of them is lexical. The existing literature suggests that alignment has a significant role in communication between humans, and is also beneficial in human–agent communication. Various methods have been proposed in the past to measure lexical alignment in human–human conversations, and also to implement them in conversational agents. In this research, we carry out an analysis of the existing methods to measure lexical alignment and also dissect methods to implement it in a conversational agent for personalizing human–agent interactions. We propose a new set of criteria that such methods should meet and discuss the possible improvements that can be made to existing methods.

This work reimplements a recent semantic bootstrapping child language acquisition (CLA) model, which was originally designed for English, and trains it to learn a new language: Hebrew. The model learns from pairs of utterances and logical forms as meaning representations, and acquires both syntax and word meanings simultaneously. The results show that the model mostly transfers to Hebrew, but that a number of factors, including the richer morphology in Hebrew, makes the learning slower and less robust. This suggests that a clear direction for future work is to enable the model to leverage the similarities between different word forms.

The prevalence of conversational interfaces is rapidly rising, since improved algorithms allow for remarkable proficiency in understanding and generating natural language. This also holds for Conversational Recommender Systems (CRS), that benefit from information being provided by the user in the course of the dialogue to offer personalized recommendations. However, the challenge remains eliciting the user's characteristics and preferences in a way that leads to the most optimal user experience. Hence, the current research was aimed at investigating the effect of different Preference Elicitation (PE) methods on the user experience of a CRS. We introduce two axes across which PE methods can be classified, namely the degree of system prompt guidance and the level of user input restriction. We built three versions of a CRS to conduct a between-subjects experiment which compared three conditions: high guidance-high restriction, high guidance-low restriction and low guidance-low restriction. We tested their effect on ten constructs of user experience measures on 66 European participants, all working in agriculture or forestry.The study did not find any significant effects of the three preference elicitation methods on all user experience constructs collected through questionnaires. However, we did find significant differences in terms of the objective measures chat duration (Speed), response time (Cognitive Demand) and recommendation performance (Accuracy of Recommended Items). Regarding the recommendation performance, it was found that the preference elicitation methods with high guidance led to a higher match score than the condition with low guidance. The certainty score was highest in the condition with high guidance and high input restriction. Finally, we found through a question at the end of the conversation that users who were satisfied with the recommendation responded more positively to six out of ten user experience constructs. This suggests that satisfaction with the recommendation performance is a crucial factor in the user experience of CRSs.

Speech intelligibility (SI) prediction models are a valuable tool for the development of speech processing algorithms for hearing aids or consumer electronics. For the use in realistic environments it is desirable that the SI model is non-intrusive (does not require separate input of original and degraded speech, transcripts or a-priori knowledge about the signals) and does a binaural processing of the audio signals. Most of the existing SI models do not fulfill all of these criteria. In this study, we propose an SI model based on phone probabilities obtained from a deep neural net. The model comprises a binaural enhancement stage for prediction of the speech recognition threshold (SRT) in realistic acoustic scenes. In the first part of the study, SRT predictions in different spatial configurations are compared to the results from normal-hearing listeners. On average, our approach produces lower errors and higher correlations compared to three intrusive baseline models. In the second part, we explore if measures relevant in spatial hearing, i.e., the intelligibility level difference (ILD) and the binaural ILD (BILD), can be predicted with our modeling approach. We also investigate if a language mismatch between training and testing the model plays a role when predicting ILD and BILD. This point is especially important for low-resource languages, where not thousands of hours of language material are available for training. Binaural benefits are predicted by our model with an error of 1.5 dB. This is slightly higher than the error with a competitive baseline MBSTOI (1.1 dB), but does not require separate input of original and degraded speech. We also find that good binaural predictions can be obtained with models that are not specifically trained with the target language.