no code implementations • 11 May 2023 • Jinglun Cai, Monica Sunkara, Xilai Li, Anshu Bhatia, Xiao Pan, Sravan Bodapati
Masked Language Models (MLMs) have proven to be effective for second-pass rescoring in Automatic Speech Recognition (ASR) systems.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
no code implementations • 5 May 2023 • Nilaksh Das, Monica Sunkara, Sravan Bodapati, Jinglun Cai, Devang Kulshreshtha, Jeff Farris, Katrin Kirchhoff
Internal language model estimation (ILME) has been proposed to mitigate this bias for autoregressive models such as attention-based encoder-decoder and RNN-T.
no code implementations • 15 Nov 2022 • Rishabh Bhardwaj, George Polovets, Monica Sunkara
Semi-parametric Nearest Neighbor Language Models ($k$NN-LMs) have produced impressive gains over purely parametric LMs, by leveraging large-scale neighborhood retrieval over external memory datastores.
no code implementations • 18 Oct 2022 • Saket Dingliwal, Monica Sunkara, Sravan Bodapati, Srikanth Ronanki, Jeff Farris, Katrin Kirchhoff
End-to-end speech recognition models trained using joint Connectionist Temporal Classification (CTC)-Attention loss have gained popularity recently.
no code implementations • 10 Sep 2021 • Dhanush Bekal, Ashish Shenoy, Monica Sunkara, Sravan Bodapati, Katrin Kirchhoff
Accurate recognition of slot values such as domain specific words or named entities by automatic speech recognition (ASR) systems forms the core of the Goal-oriented Dialogue Systems.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 21 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.
no code implementations • 10 Mar 2021 • Nilaksh Das, Sravan Bodapati, Monica Sunkara, Sundararajan Srinivasan, Duen Horng Chau
Training deep neural networks for automatic speech recognition (ASR) requires large amounts of transcribed speech.
no code implementations • 12 Feb 2021 • Monica Sunkara, Chaitanya Shivade, Sravan Bodapati, Katrin Kirchhoff
We propose an efficient and robust neural solution for ITN leveraging transformer based seq2seq models and FST-based text normalization techniques for data preparation.
no code implementations • 3 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.
no code implementations • WS 2020 • Monica Sunkara, Srikanth Ronanki, Kalpit Dixit, Sravan Bodapati, Katrin Kirchhoff
We also present techniques for domain and task specific adaptation by fine-tuning masked language models with medical domain data.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2