Most voice biometric systems are dependent on the language of the user. However, if the idea is to create an all‐inclusive and reliable system that uses speech as its input, then they should be able to recognise people regardless of language or accent. Thus, this paper investigates the effects of languages on speaker identification systems and the phonetic impact on their performance. The experiments are performed using three widely spoken languages which are Portuguese, English, and Chinese. The Mel‐Frequency Cepstrum Coefficients and its Deltas are extracted from those languages. Also, this paper expands the research study of fuzzy models in the speaker recognition field, using a Fuzzy C‐Means and Fuzzy k‐Nearest Neighbours and comparing them with k‐Nearest Neighbours and Support Vector Machines. Results with more languages decreases the accuracy from 92% to 85.59%, but further investigation suggests it is caused by the number of classes. A phonetic investigation finds no relation between the phonemes and the results. Finally, fuzzy methods offer more flexibility and in some cases, even better results compared to their crisp version. Therefore, the biometric system presented here is not affected by multilingual environments.

Speech is a crucial for human communication and combined with the evolution of instant messaging in voice format as well as automated chatbots, its importance is greater. While the majority of speech technologies have achieved high accuracy, they fail when tested for accents that deviate from the “standard” of a language. This becomes more concerning for languages that lack on datasets and have scarce literature, like Brazilian Portuguese. Thus, this paper proposes a methodology to collect and release a speech dataset for Brazilian Portuguese. The method explores the availability of data and information in video platforms, and automatically extracts the audio from TEDx Talks.

Speech is a fundamental component of human communication and has become increasingly important with the widespread adoption of voice-based technologies, text messaging systems, Chatbots, and Large Language Models (LLMs). While AI systems are going through exponential breakthroughs, they often prioritize the dominant variant of a language. Using the TEDx Talks Brazilian Accents (TTBAcc) dataset, this research provides an acoustic analysis of two Brazilian Portuguese accents: Nordestino and Paulistano and an unsupervised accent clustering to find distinctive characteristics and investigate factors that can potentially support the monitoring of emerging and endangered accents. The acoustic analysis compares rhotics and vowels produced by speakers of both accents. The analysis of rhotics provided limited evidence of meaningful difference between accents. In contrast, the vowels exhibit significant differences in the fundamental frequency ($f_0$), the first and second formant frequencies ($F_1$ and $F_2$) and intensity, suggesting that these acoustic features may be more effective to distinguish between the investigated accents. For accent clustering, $f_0$, length, volume, and formant frequencies ($F_1$ and $F_2$) are used as inputs. Among the evaluated approaches, K-Means, with $K=38$ achieves the best performance, obtaining a Rand Score of 0.775 ± 0.028 and 0.524 ± 0.032 homogeneity, the best performing model. The optimal number of cluster exceeds the number of classified accents, which may indicate either an over-segmentation of data or a finer-grained phonetic variation captured in acoustic features. These findings highlight the potential of using acoustic features for investigating the variation in regional accents, and provides a basis for further research in monitoring emerging and endangered accents.