Luiz W. P. Biscainho

Pedro H. L. Leite, Pedro Benevenuto Valadares, Luiz W. P. Biscainho

Regional accent classification in Brazilian Portuguese (pt-BR) suffers from the need for reliable labeling. While large self-supervised learning (SSL) speech models are powerful, their training pipelines dilute sociophonetic information, since accent labels are generally not reliable or are not used in training objectives. This work introduces a novel workflow for feature extraction using only acoustic labels. By isolating explicit regional accent landmarks and using a phoneme-based forced aligner (ZIPA), our targeted feature set captures dialectal variance more effectively than utterance embeddings, demonstrating that localized features can outperform general-purpose architectures on accent-related tasks using minimal and objective data labels.

Leonardo A. Fanzeres, Adriana S. Vivacqua, Luiz W. P. Biscainho

Human perception of surrounding events is strongly dependent on audio cues. Thus, acoustic insulation can seriously impact situational awareness. We present an exploratory study in the domain of assistive computing, eliciting requirements and presenting solutions to problems found in the development of an environmental sound recognition system, which aims to assist deaf and hard of hearing people in the perception of sounds. To take advantage of smartphones computational ubiquity, we propose a system that executes all processing on the device itself, from audio features extraction to recognition and visual presentation of results. Our application also presents the confidence level of the classification to the user. A test of the system conducted with deaf users provided important and inspiring feedback from participants.

Markus V. S. Lima, Wallace A. Martins, Leonardo O. Nunes et al.

This paper proposes an efficient method based on the steered-response power (SRP) technique for sound source localization using microphone arrays: the volumetric SRP (V-SRP). As compared to the SRP, by deploying a sparser volumetric grid, the V-SRP achieves a significant reduction of the computational complexity without sacrificing the accuracy of the location estimates. By appending a fine search step to the V-SRP, its refined version (RV-SRP) improves on the compromise between complexity and accuracy. Experiments conducted in both simulated- and real-data scenarios demonstrate the benefits of the proposed approaches. Specifically, the RV-SRP is shown to outperform the SRP in accuracy at a computational cost of about ten times lower.