Search Results for author: Srikanth Ronanki

Found 10 papers, 1 papers with code

Adapting Long Context NLM for ASR Rescoring in Conversational Agents

no code implementations21 Apr 2021 Ashish Shenoy, Sravan Bodapati, Monica Sunkara, Srikanth Ronanki, Katrin Kirchhoff

Neural Language Models (NLM), when trained and evaluated with context spanning multiple utterances, have been shown to consistently outperform both conventional n-gram language models and NLMs that use limited context.

Intent Classification Language Modelling +1

Multimodal Semi-supervised Learning Framework for Punctuation Prediction in Conversational Speech

no code implementations3 Aug 2020 Monica Sunkara, Srikanth Ronanki, Dhanush Bekal, Sravan Bodapati, Katrin Kirchhoff

Experiments conducted on the Fisher corpus show that our proposed approach achieves ~6-9% and ~3-4% absolute improvement (F1 score) over the baseline BLSTM model on reference transcripts and ASR outputs respectively.

Data Augmentation Frame

Fine-grained robust prosody transfer for single-speaker neural text-to-speech

no code implementations4 Jul 2019 Viacheslav Klimkov, Srikanth Ronanki, Jonas Rohnke, Thomas Drugman

However, when trained on a single-speaker dataset, the conventional prosody transfer systems are not robust enough to speaker variability, especially in the case of a reference signal coming from an unseen speaker.

Effect of data reduction on sequence-to-sequence neural TTS

no code implementations15 Nov 2018 Javier Latorre, Jakub Lachowicz, Jaime Lorenzo-Trueba, Thomas Merritt, Thomas Drugman, Srikanth Ronanki, Klimkov Viacheslav

Recent speech synthesis systems based on sampling from autoregressive neural networks models can generate speech almost undistinguishable from human recordings.

Speech Synthesis

Median-Based Generation of Synthetic Speech Durations using a Non-Parametric Approach

no code implementations22 Aug 2016 Srikanth Ronanki, Oliver Watts, Simon King, Gustav Eje Henter

This paper proposes a new approach to duration modelling for statistical parametric speech synthesis in which a recurrent statistical model is trained to output a phone transition probability at each timestep (acoustic frame).

Frame Speech Synthesis

DNN-based Speech Synthesis for Indian Languages from ASCII text

no code implementations18 Aug 2016 Srikanth Ronanki, Siva Reddy, Bajibabu Bollepalli, Simon King

These methods first convert the ASCII text to a phonetic script, and then learn a Deep Neural Network to synthesize speech from that.

Speech Synthesis Text-To-Speech Synthesis

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