The use of intelligent virtual assistants for human-machine communication is spreading across multiple applications. The latest breakthroughs in fields such as Natural Language Processing (NLP) or Natural Language Generation (NLG) make it possible to communicate with machines in a more natural and fluent way and in broader contexts, normalizing voice-based interactions with machines. These advances also lead to the appearance of new issues never seen before, especially when this technology extends to public services such as administration, education or health. The transfer of personal data, the opacity of decisions, the presence of bias or the exclusion of groups are critical aspects that cannot be controlled exclusively by economic interests. The design of conversational assistant solutions must be within an ethical, legal, socio-economic and cultural (ELSEC) framework, and it must be ensured that it preserves the dignity, freedom and autonomy of the users. In this paper, we analyse the Artificial Intelligence (AI) European regulatory framework, the issues that appear when designing and developing AI-based conversational solutions for e-health, and we present recommendations based on our experience and on the reflection from an ethical point of view.
Active Learning, that is, assigning the responsibility of learning to the students, is an important tool in education as it makes the students become engaged in and think about the things they do. A similar concept was adopted in the context of Machine Learning as a means to reduce the annotation effort by selecting the examples that are most relevant or provide more information at a given time. Most studies on this subject focus on the learner’s performance. However, in interactive scenarios, the teacher’s experience is also a relevant aspect, as it affects their willingness to interact with artificial learners. In this paper, we address that aspect by performing a case study in a language grounding scenario, in which humans have to engage in dialog with a learning agent and teach it how to recognize observations of certain objects. Overall, the results of our experiments show that humans prefer to interact with an active learner, as it seems more intelligent, gives them a better perception of its knowledge, and makes the dialog more natural and enjoyable.
Different studies have shown the importance of visual cues throughout the speech perception process. In fact, the development of audiovisual approaches has led to advances in the field of speech technologies. However, although noticeable results have recently been achieved, visual speech recognition remains an open research problem. It is a task in which, by dispensing with the auditory sense, challenges such as visual ambiguities and the complexity of modeling silence must be faced. Nonetheless, some of these challenges can be alleviated when the problem is approached from a speaker-dependent perspective. Thus, this paper studies, using the Spanish LIP-RTVE database, how the estimation of specialized end-to-end systems for a specific person could affect the quality of speech recognition. First, different adaptation strategies based on the fine-tuning technique were proposed. Then, a pre-trained CTC/Attention architecture was used as a baseline throughout our experiments. Our findings showed that a two-step fine-tuning process, where the VSR system is first adapted to the task domain, provided significant improvements when the speaker adaptation was addressed. Furthermore, results comparable to the current state of the art were reached even when only a limited amount of data was available.
Biometric systems are exposed to spoofing attacks which may compromise their security, and automatic speaker verification (ASV) is no exception. To increase the robustness against such attacks, anti-spoofing systems have been proposed for the detection of spoofed audio attacks. However, most of these systems can not capture long-term feature dependencies and can only extract local features. While transformers are an excellent solution for the exploitation of these long-distance correlations, they may degrade local details. On the contrary, convolutional neural networks (CNNs) are a powerful tool for extracting local features but not so much for capturing global representations. The conformer is a model that combines the best of both techniques, CNNs and transformers, to model both local and global dependencies and has been used for speech recognition achieving state-of-the-art performance. While conformers have been mainly applied to sequence-to-sequence problems, in this work we make a preliminary study of their adaptation to a binary classification task such as anti-spoofing, with focus on synthesis and voice-conversion-based attacks. To evaluate our proposals, experiments were carried out on the ASVspoof 2019 logical access database. The experimental results show that the proposed system can obtain encouraging results, although more research will be required in order to outperform other state-of-the-art systems.
With the advent of technology, the availability of linguistic data in digital format has been increasingly encouraged to facilitate its use not only in different areas of Linguistics but also in related areas, such as natural language processing. Inspired by a protocol for digitizing the NURC (‘Cultured Linguistic Urban Norm’) project collection — one of the most influential in Brazilian Linguistics —, this paper aims to present the text-to-speech alignment process of the NURC-S˜ao Paulo Minimal Corpus. This subcorpus comprises 21 audio files and audio-aligned multilevel transcripts according to linguistically moti-vated intonation units ( ≈ 18 hours, ≈ 155k words), covering three text genres. The dataset — currently used to evaluate methods for processing the entire NURC-SP corpus — is publicly available on the Portulan Clarin repository [CC BY-NC-ND 4.0] (https://hdl.handle.net/21.11129/0000-000F-73CA-C
