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researcher

A. Ramakrishnan

13 papers hereh-index 303.1k citations208 works total

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

author position
  • middle author1
  • last author11

Across the 12 of 13 papers where every author was matched, so the position is known.

fields
  • cs.CV5
  • eess.AS3
  • cs.CL2
  • cs.SD1
  • eess.SP1
  • q-bio.NC1
same name
  • A. Ramakrishnan — 1 paper, h 4
  • A. Ramakrishnan — 1 paper, h 3

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
20122024
most citedUnsupervised Domain Adaptation Schemes for Building ASR in Low-resource Languages

5 citations · 13 across the 7 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

eess.AS2018

Pitch-synchronous DCT features: A pilot study on speaker identification

Amit Meghanani, A G Ramakrishnan

We propose a new feature, namely, pitchsynchronous discrete cosine transform (PS-DCT), for the task of speaker identification. These features are obtained directly from the voiced…

eess.AS2018

Using Monte Carlo dropout for non-stationary noise reduction from speech

Nazreen P. M., A. G. Ramakrishnan

In this work, we propose the use of dropout as a Bayesian estimator for increasing the generalizability of a deep neural network (DNN) for speech enhancement. By using Monte Carlo…

cs.SD2018

Subjective and objective experiments on the influence of speaker's gender on the unvoiced segments

A Madhavaraj, T V Ananthapadmanabha, A G Ramakrishnan

Subjective and objective experiments are conducted to understand the extent to which a speaker's gender influences the acoustics of unvoiced (U) sounds. U segments of utterances ar…

eess.AS2018

DNN Based Speech Enhancement for Unseen Noises Using Monte Carlo Dropout

Nazreen P M, A G Ramakrishnan

In this work, we propose the use of dropouts as a Bayesian estimator for increasing the generalizability of a deep neural network (DNN) for speech enhancement. By using Monte Carlo…

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