## 1 Introduction
This proposal can expand the use of VAs to a wide range of remote spaces that require greater operational autonomy or the possibility of offline operation, which could be highly useful in the situations above and for low-income populations with limited Internet access. Reducing the response time for queries made through VAs could significantly impact critical processes and user experience (UX) improvement. In the case of mass adoption, there could be a reduction in the eco-environmental impact, as global annual energy consumption could decrease in theory by the order of Megawatts (MW) or Gigawatts (GW). In the proposed implementation, several aspects of VA operation need to be considered. An initial aspect is that these require the process of speech-to-text translation using the STT (Speech To Text) protocol. This process can take place either within the local device or on servers located in the cloud provided by the Internet Service Provider (ISP). Thus, traffic in these VAs is captured as plain text in order to identify the queries issued and the potential storage in the cache within the prototype, allowing subsequent responses to be served from the cache or encapsulated and sent through the channel to the cloud to obtain the response from that point. This alternative of using cache storage has been widely suggested due to its ability to relieve traffic load with the ISP and minimize access latency, as seen in other increasingly popular applications like live peer-to-peer (P2P) streaming or machine-to-machine (M2M) communications [12]. [PERSON] (2022) mentions that text processing by VAs can present error rates [8]. For this reason, in this study, a parallel process is proposed when capturing traffic in text format, consisting of sending the query to an AI scheme such as GPT-4 via an API, enabling the interpretation of content using natural language processing (NLP) techniques. The results obtained from this source would be used to feed a cache database complementarily. On the other hand, this type of development is implemented using lightweight and low-cost hardware, leading to the use of light techniques and protocols that allow for energy consumption optimization. In the state of the art, four lines of research on AVs were highlighted, which are presented in Figure 1.  
Regarding speech recognition processes, ref. [13] presents three tools for Automatic Speech Recognition (ASR) and text translation locally, avoiding the need for an Internet connection. These tools are implemented on Raspberry Pi (Raspberry Pi Foundation, UK) using Python, including validation with a word error rate (WER). The results show Vosk as one of the most reliable applications, which is why it is the tool used in this work. Additionally, [PERSON]'s work presents a complete mixed-signal chip system capable of directly interacting with an analog microphone, performing keyword detection (KWS) and Speaker Verification (SV) through algorithmic optimization and integrated single-chip design [14]. Although this proposal is interesting, it is not oriented toward cache storage. [PERSON] et al. designed and implemented an AIoT system to assist visually impaired individuals, which consists of a smart cane for monitoring various variables and glasses for text and object recognition. This system provides substantial help to people with this disability as it transmits captured information through a VA [15]. While this work bears some relation to the proposal presented here, it is not aimed at optimizing energy consumption, given the number of sensors and elements needed for its operation. A couple of communication platforms created for home automation systems are presented in [16; 17], which can operate offline and automatically perform various tasks in a household. However, these solutions need certain functionalities as they do not incorporate cache storage. Furthermore, ref. [18] proposes a communication interface using ASR, applied to a  
Figure 1: Classification of lines of research on VAs. local network without Internet access.