Papers by Karan Singla
Towards end-2-end learning for predicting behavior codes from spoken utterances in psychotherapy conversations (2020.acl-main)
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| Challenge: | Xu and Sarikaya, 2014) proposes a framework for predicting utterance level labels directly from speech features. |
| Approach: | They propose a framework for predicting utterance level labels directly from speech features using a pretrained Speech-2-Vector encoder as bottleneck. |
| Outcome: | The proposed model outperforms state-of-the-art approaches which use transcribed text for the task of predicting psychotherapy-relevant behavior codes. |
Visual-Aware Speech Recognition for Noisy Scenarios (2025.emnlp-main)
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| Challenge: | Existing audio-only models that use visual cues for transcription struggle in noisy environments. |
| Approach: | They propose a method that correlates visual cues with noise sources to improve transcription by filtering speech from noise and predicting noise labels in video inputs. |
| Outcome: | The proposed model improves transcription by correlating noise sources to visual cues in audio inputs. |
E2E Spoken Entity Extraction for Virtual Agents (2023.emnlp-industry)
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| Challenge: | Extensive research has been done to recognize entities in spoken input. |
| Approach: | They propose to fine-tune pre-trained speech encoders to extract spoken entities directly from speech without the need for text transcription. |
| Outcome: | The proposed approach outperforms the 2-step approach for extracting spoken entities from human-computer conversations. |
A Multi-task Approach to Learning Multilingual Representations (P18-2)
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| Challenge: | Using a multi-task model, we learn word and sentence embeddings in a single task. |
| Approach: | They propose a multi-task modeling approach that trains a skip-gram model and a cross-lingual sentence similarity model to learn word and sentence embeddings together. |
| Outcome: | The proposed model can learn word and sentence embeddings in a multilingual distributed representations of text using a cross-lingual sentence similarity model. |