Author: Nora Brooks

Authors: Bingyu Li、Da Zhang、Zhiyuan Zhao、Junyu Gao、Yuan Yuan Paper: https://arxiv.org/abs/2408.07891 Introduction The rapid growth of textual information on social media has made sentiment analysis a crucial tool for understanding social opinions and product evaluations. Traditional sentiment analysis models, while effective, often struggle with integrating diverse semantic information and lack interpretability. This paper introduces a novel approach by combining quantum mechanics (QM) principles with deep learning models to enhance text sentiment analysis. The proposed Quantum-inspired Interpretable Text Sentiment Analysis Architecture (QITSA) leverages the commonalities between text representation and QM principles to improve model accuracy and interpretability. Related Work Text Sentiment Analysis Text…

Authors: Matthias Bartolo Paper: https://arxiv.org/abs/2408.06804 Introduction Speaker identification (SID) is a crucial task in various applications such as forensics, security, and personalized services. It involves determining a speaker’s identity from an audio sample chosen from a pool of known speakers. This research delves into the essential components of SID, focusing on feature extraction and classification. The study emphasizes the use of Mel Spectrogram and Mel Frequency Cepstral Coefficients (MFCC) for feature extraction and evaluates six different model architectures to determine their performance. Feature Extraction Feature extraction is a critical step in speech analysis, transforming raw audio data into useful features.…