no code implementations • 8 Nov 2023 • Karan Singla, Shahab Jalalvand, Yeon-Jun Kim, Antonio Moreno Daniel, Srinivas Bangalore, Andrej Ljolje, Ben Stern
Recent studies have made some progress in refining end-to-end (E2E) speech recognition encoders by applying Connectionist Temporal Classification (CTC) loss to enhance named entity recognition within transcriptions.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 16 Feb 2023 • Karan Singla, Yeon-Jun Kim, Srinivas Bangalore
In human-computer conversations, extracting entities such as names, street addresses and email addresses from speech is a challenging task.
no code implementations • 20 Apr 2022 • Karan Singla, Daniel Pressel, Ryan Price, Bhargav Srinivas Chinnari, Yeon-Jun Kim, Srinivas Bangalore
In this paper, we propose a novel architecture for multi-modal speech and text input.
no code implementations • 29 Mar 2022 • Karan Singla, Shahab Jalalvand, Yeon-Jun Kim, Ryan Price, Daniel Pressel, Srinivas Bangalore
Person name capture from human speech is a difficult task in human-machine conversations.
no code implementations • 15 Jun 2021 • Zhuohao Chen, Nikolaos Flemotomos, Karan Singla, Torrey A. Creed, David C. Atkins, Shrikanth Narayanan
In particular, we model the global quality as a linear function of the local quality scores, which allows us to update the segment-level quality estimates based on the session-level quality prediction.
no code implementations • 22 Feb 2021 • Nikolaos Flemotomos, Victor R. Martinez, Zhuohao Chen, Karan Singla, Victor Ardulov, Raghuveer Peri, Derek D. Caperton, James Gibson, Michael J. Tanana, Panayiotis Georgiou, Jake Van Epps, Sarah P. Lord, Tad Hirsch, Zac E. Imel, David C. Atkins, Shrikanth Narayanan
With the growing prevalence of psychological interventions, it is vital to have measures which rate the effectiveness of psychological care to assist in training, supervision, and quality assurance of services.
no code implementations • 19 Aug 2020 • Victor R. Martinez, Krishna Somandepalli, Karan Singla, Anil Ramanakrishna, Yalda T. Uhls, Shrikanth Narayanan
To date, we are the first to show that language used in movie scripts is a strong indicator of violent content, and that there are systematic portrayals of certain demographics as victims and perpetrators in a large dataset.
no code implementations • ACL 2020 • Karan Singla, Zhuohao Chen, David Atkins, Shrikanth Narayanan
Spoken language understanding tasks usually rely on pipelines involving complex processing blocks such as voice activity detection, speaker diarization and Automatic speech recognition (ASR).
no code implementations • 1 May 2019 • Victor R. Martinez, Anil Ramakrishna, Ming-Chang Chiu, Karan Singla, Shrikanth Narayanan
In this work, we describe our submission for the 2019 Sentiment, Emotion and Cognitive state (SEC) pilot task of the LORELEI project.
no code implementations • ACL 2018 • Karan Singla, Dogan Can, Shrikanth Narayanan
We present a novel multi-task modeling approach to learning multilingual distributed representations of text.
Cross-Lingual Document Classification Document Classification +5
no code implementations • WS 2017 • Karan Singla, Evgeny Stepanov, Ali Orkan Bayer, Giuseppe Carenini, Giuseppe Riccardi
Summarization of spoken conversations is a challenging task, since it requires deep understanding of dialogs.
no code implementations • WS 2017 • Jacqueline Brixey, Rens Hoegen, Wei Lan, Joshua Rusow, Karan Singla, Xusen Yin, Ron artstein, Anton Leuski
We present the implementation of an autonomous chatbot, SHIHbot, deployed on Facebook, which answers a wide variety of sexual health questions on HIV/AIDS.
no code implementations • ACL 2017 • Anil Ramakrishna, Victor R. Mart{\'\i}nez, Mal, Nikolaos rakis, Karan Singla, Shrikanth Narayanan
We examine differences in portrayal of characters in movies using psycholinguistic and graph theoretic measures computed directly from screenplays.