Raj Sangani

Selected Publications

1. ACM SIGCHI 24

   Do People Mirror Emotion Differently with a Human or TTS Voice? Comparing Listener Ratings and Word Embeddings

   Michelle Cohn, Grisha Bandodkar, Raj Bharat Sangani, and 2 more authors

   2024

   Abs Bib PDF

   Speakers increasingly communicate with technology using spoken language and display behaviors from human-human interaction, such as mirroring the pronunciation patterns of text-to-speech (TTS) voices. Yet, the magnitude and directionality of mirroring varies across prior studies comparing human and TTS addressees. The current study uses two approaches to assess mirroring of emotionally expressive speech produced by a human and a TTS voice. We compare AXB perceptual similarity (holistic human listener judgment, n=109 raters), and distance in latent space (wav2vec 2.0) on a dataset of single word shadowing in a cohort of 36 United States English speakers. Results show that both AXB and wav2vec 2.0 capture mirroring toward emotional prosody for both human and TTS voices. We discuss these findings in terms of theories of computer personification and emotional mirroring, as well as their contributions toward methodological advancements in phonetic entrainment.

   @article{1234,
     author = {Cohn, Michelle and Bandodkar, Grisha and Sangani, Raj Bharat and Predeck, Kristin and Zellou, Georgia},
     title = {Do People Mirror Emotion Differently with a Human or TTS Voice? Comparing Listener Ratings and Word Embeddings},
     year = {2024},
     isbn = {9798400703317},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3613905.3650757},
     doi = {10.1145/3613905.3650757},
     booktitle = {Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems},
     articleno = {110},
     numpages = {10},
     keywords = {Embeddings, Emotional Expressiveness, Phonetic Mirroring, Text-to-speech (TTS) Voices},
     location = {
     Honolulu
     HI
     USA
     },
     series = {CHI EA '24},
   }

2. IEEE Big Data

   Large-Scale Knowledge Synthesis and Complex Information Retrieval from Biomedical Documents

   Shreya Saxena, Raj Sangani, Siva Prasad, and 4 more authors

   Dec 2022

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   Recent advances in the healthcare industry have led to an abundance of unstructured data, making it challenging to perform tasks such as efficient and accurate information retrieval at scale. Our work offers an all-in-one scalable solution for extracting and exploring complex information from large-scale research documents, which would otherwise be tedious. First, we briefly explain our knowledge synthesis process to extract helpful information from unstructured text data of research documents. Then, on top of the knowledge extracted from the documents, we perform complex information retrieval using three major components- Paragraph Retrieval, Triplet Retrieval from Knowledge Graphs, and Complex Question Answering (QA). These components combine lexical and semantic-based methods to retrieve paragraphs and triplets and perform faceted refinement for filtering these search results. The complexity of biomedical queries and documents necessitates using a QA system capable of handling queries more complex than factoid queries, which we evaluate qualitatively on the COVID-19 Open Research Dataset (CORD-19) to demonstrate the effectiveness and value- add.

   @article{9588834,
     author = {Saxena, Shreya and Sangani, Raj and Prasad, Siva and Kumar, Shubham and Athale, Mihir and Awhad, Rohan and Vaddina, Vishal},
     year = {2022},
     month = dec,
     booktitle = {2022 IEEE International Conference on Big Data (Big Data)},
     title = {Large-Scale Knowledge Synthesis and Complex Information Retrieval from Biomedical Documents},
     volume = {},
     number = {},
     pages = {2364-2369},
     keywords = {COVID-19;Industries;Semantic search;Medical services;Big Data;Information filters;Question answering (information retrieval);Information-Retrieval;Knowledge-Synthesis;Semantic-Retrieval;Question-Answering;CORD-19},
     doi = {10.1109/BigData55660.2022.10020725},
   }

3. IEEE Xplore

   Comparing Deep Sentiment Models using Quantified Local Explanations

   Raj Bharat Sangani, Apratim Shukla, and Radhika B. Selvamani

   Nov 2021

   Abs Bib PDF

   Online judge systems that enable the automatic evaluation of predictive models face the problem of choosing the best model among those with near equal accuracy measures. In such scenarios, interpreting competitive models may provide a better insight towards robustness. Sentiment analysis is a well- explored domain with many available approaches and libraries. Despite this, it is context-sensitive and poses many challenges to the research community and thus deems fit to our analysis. Selecting a model with the highest accuracy may not be satisfactory, especially when the decision margins are too narrow. Our comparative study on models with similar accuracies and f1-scores but distinct underlying architectures incorporate custom metrics and evaluation methods to assess their performance on a sentiment analysis task. We have proposed to include human judgment in an online judge system using a feedback acquisition mechanism, presenting explanations for model decisions on selected test cases. Our initial experimental findings indicate that evaluating model robustness by incorporating a well-defined human feedback mechanism, using model agnostic approaches encourages online judge systems to make explainable decisions. The p-value associated with a paired t-Test on the feedback collected for model preference indicates a significant preference for the model supported by our metrics.

   @article{9588835,
     author = {Sangani, Raj Bharat and Shukla, Apratim and Selvamani, Radhika B.},
     booktitle = {2021 Smart Technologies, Communication and Robotics (STCR)},
     title = {Comparing Deep Sentiment Models using Quantified Local Explanations},
     year = {2021},
     volume = {},
     number = {},
     pages = {1-6},
     month = nov,
     doi = {https://doi.org/10.1109/STCR51658.2021.9588834},
     publisher = {IEEE Xplore},
   }