Tootfinder

Evaluating the Representation of Vowels in Wav2Vec Feature Extractor: A Layer-Wise Analysis Using MFCCs
Domenico De Cristofaro, Vincenzo Norman Vitale, Alessandro Vietti
https://arxiv.org/abs/2508.17914

Evaluating the Representation of Vowels in Wav2Vec Feature Extractor: A Layer-Wise Analysis Using MFCCs
Automatic Speech Recognition has advanced with self-supervised learning, enabling feature extraction directly from raw audio. In Wav2Vec, a CNN first transforms audio into feature vectors before the transformer processes them. This study examines CNN-extracted information for monophthong vowels using the TIMIT corpus. We compare MFCCs, MFCCs with formants, and CNN activations by training SVM classifiers for front-back vowel identification, assessing their classification accuracy to evaluate pho…

Per-Row Activation Counting on Real Hardware: Demystifying Performance Overheads
Jumin Kim, Seungmin Baek, Minbok Wi, Hwayong Nam, Michael Jaemin Kim, Sukhan Lee, Kyomin Sohn, Jung Ho Ahn
https://arxiv.org/abs/2507.05556

Per-Row Activation Counting on Real Hardware: Demystifying Performance Overheads
Per-Row Activation Counting (PRAC), a DRAM read disturbance mitigation method, modifies key DRAM timing parameters, reportedly causing significant performance overheads in simulator-based studies. However, given known discrepancies between simulators and real hardware, real-machine experiments are vital for accurate PRAC performance estimation. We present the first real-machine performance analysis of PRAC. After verifying timing modifications on the latest CPUs using microbenchmarks, our analy…