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20172022
most citedSpeaker identification from the sound of the human breath

10 citations · 25 across the 12 of their papers we have counts for

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12 papers · 1 filter

cs.SD20223 cited

Describing emotions with acoustic property prompts for speech emotion recognition

Hira Dhamyal, Benjamin Elizalde, Soham Deshmukh +3

Emotions lie on a broad continuum and treating emotions as a discrete number of classes limits the ability of a model to capture the nuances in the continuum. The challenge is how…

cs.SD20223 cited

Unifying the Discrete and Continuous Emotion labels for Speech Emotion Recognition

Roshan Sharma, Hira Dhamyal, Bhiksha Raj +1

Traditionally, in paralinguistic analysis for emotion detection from speech, emotions have been identified with discrete or dimensional (continuous-valued) labels. Accordingly, mod…

cs.SD2021

An Overview of Techniques for Biomarker Discovery in Voice Signal

Rita Singh, Ankit Shah, Hira Dhamyal

This paper reflects on the effect of several categories of medical conditions on human voice, focusing on those that may be hypothesized to have effects on voice, but for which the…

cs.SD2021

Generalized Spoofing Detection Inspired from Audio Generation Artifacts

Yang Gao, Tyler Vuong, Mahsa Elyasi +2

State-of-the-art methods for audio generation suffer from fingerprint artifacts and repeated inconsistencies across temporal and spectral domains. Such artifacts could be well capt…

cs.SD20201 cited

Detection and Evaluation of human and machine generated speech in spoofing attacks on automatic speaker verification systems

Yang Gao, Jiachen Lian, Bhiksha Raj +1

Automatic speaker verification (ASV) systems utilize the biometric information in human speech to verify the speaker's identity. The techniques used for performing speaker verifica…

cs.SD2020

Masked Proxy Loss For Text-Independent Speaker Verification

Jiachen Lian, Aiswarya Vinod Kumar, Hira Dhamyal +2

Open-set speaker recognition can be regarded as a metric learning problem, which is to maximize inter-class variance and minimize intra-class variance. Supervised metric learning c…