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Shrikanth S. Narayanan

University of Southern California

15 papers hereh-index 171k citations62 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author5
  • last author10

Across the 15 of 15 papers where every author was matched, so the position is known.

fields
  • eess.AS6
  • cs.SD3
  • cs.CL2
  • cs.CV1
  • cs.CY1
  • eess.IV1
affiliations
  • University of Southern California
Homepage
same name
  • Shrikanth S. Narayanan — 66 papers, h 89
  • Shrikanth S. Narayanan — 44 papers, h 9
  • Shrikanth S. Narayanan — 13 papers, h 3
  • Shrikanth S. Narayanan — 11 papers, h 1
  • Shrikanth S. Narayanan — 10 papers, h 4
  • Shrikanth S. Narayanan — 8 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192026
most citedLeveraging Open Data and Task Augmentation to Automated Behavioral Coding of Psychotherapy Conversations in Low-Resource Scenarios

3 citations · 6 across the 12 of their papers we have counts for

collaborators
Showing cs.SDShow all

3 papers · 1 filter

cs.SD2023

Unlocking Foundation Models for Privacy-Enhancing Speech Understanding: An Early Study on Low Resource Speech Training Leveraging Label-guided Synthetic Speech Content

Tiantian Feng, Digbalay Bose, Xuan Shi +1

Automatic Speech Understanding (ASU) leverages the power of deep learning models for accurate interpretation of human speech, leading to a wide range of speech applications that en…

cs.SD2022

On the Role of Visual Context in Enriching Music Representations

Kleanthis Avramidis, Shanti Stewart, Shrikanth Narayanan

Human perception and experience of music is highly context-dependent. Contextual variability contributes to differences in how we interpret and interact with music, challenging the…

cs.SD2021

Acted vs. Improvised: Domain Adaptation for Elicitation Approaches in Audio-Visual Emotion Recognition

Haoqi Li, Yelin Kim, Cheng-Hao Kuo +1

Key challenges in developing generalized automatic emotion recognition systems include scarcity of labeled data and lack of gold-standard references. Even for the cues that are lab…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.