36 citations · 98 across the 23 of their papers we have counts for
26 papers
Improving Neural Diarization through Speaker Attribute Attractors and Local Dependency Modeling
David Palzer, Matthew Maciejewski, Eric Fosler-Lussier
In recent years, end-to-end approaches have made notable progress in addressing the challenge of speaker diarization, which involves segmenting and identifying speakers in multi-ta…
Improving Speech Recognition Error Prediction for Modern and Off-the-shelf Speech Recognizers
Prashant Serai, Peidong Wang, Eric Fosler-Lussier
Modeling the errors of a speech recognizer can help simulate errorful recognized speech data from plain text, which has proven useful for tasks like discriminative language modelin…
End-to-End real time tracking of children's reading with pointer network
Vishal Sunder, Beulah Karrolla, Eric Fosler-Lussier
In this work, we explore how a real time reading tracker can be built efficiently for children's voices. While previously proposed reading trackers focused on ASR-based cascaded ap…
How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings
Shuaichen Chang, Eric Fosler-Lussier
Large language models (LLMs) with in-context learning have demonstrated remarkable capability in the text-to-SQL task. Previous research has prompted LLMs with various demonstratio…
MapQA: A Dataset for Question Answering on Choropleth Maps
Shuaichen Chang, David Palzer, Jialin Li +2
Choropleth maps are a common visual representation for region-specific tabular data and are used in a number of different venues (newspapers, articles, etc). These maps are human-r…
Towards End-to-End Integration of Dialog History for Improved Spoken Language Understanding
Vishal Sunder, Samuel Thomas, Hong-Kwang J. Kuo +3
Dialog history plays an important role in spoken language understanding (SLU) performance in a dialog system. For end-to-end (E2E) SLU, previous work has used dialog history in tex…
Tokenwise Contrastive Pretraining for Finer Speech-to-BERT Alignment in End-to-End Speech-to-Intent Systems
Vishal Sunder, Eric Fosler-Lussier, Samuel Thomas +2
Recent advances in End-to-End (E2E) Spoken Language Understanding (SLU) have been primarily due to effective pretraining of speech representations. One such pretraining paradigm is…
Building an ASR Error Robust Spoken Virtual Patient System in a Highly Class-Imbalanced Scenario Without Speech Data
Vishal Sunder, Prashant Serai, Eric Fosler-Lussier
A Virtual Patient (VP) is a powerful tool for training medical students to take patient histories, where responding to a diverse set of spoken questions is essential to simulate na…
Perceptual Loss with Recognition Model for Single-Channel Enhancement and Robust ASR
Peter Plantinga, Deblin Bagchi, Eric Fosler-Lussier
Single-channel speech enhancement approaches do not always improve automatic recognition rates in the presence of noise, because they can introduce distortions unhelpful for recogn…
Hallucination of speech recognition errors with sequence to sequence learning
Prashant Serai, Vishal Sunder, Eric Fosler-Lussier
Automatic Speech Recognition (ASR) is an imperfect process that results in certain mismatches in ASR output text when compared to plain written text or transcriptions. When plain t…
TextEssence: A Tool for Interactive Analysis of Semantic Shifts Between Corpora
Denis Newman-Griffis, Venkatesh Sivaraman, Adam Perer +2
Embeddings of words and concepts capture syntactic and semantic regularities of language; however, they have seen limited use as tools to study characteristics of different corpora…
Automated Coding of Under-Studied Medical Concept Domains: Linking Physical Activity Reports to the International Classification of Functioning, Disability, and Health
Denis Newman-Griffis, Eric Fosler-Lussier
Linking clinical narratives to standardized vocabularies and coding systems is a key component of unlocking the information in medical text for analysis. However, many domains of m…
Handling Class Imbalance in Low-Resource Dialogue Systems by Combining Few-Shot Classification and Interpolation
Vishal Sunder, Eric Fosler-Lussier
Utterance classification performance in low-resource dialogue systems is constrained by an inevitably high degree of data imbalance in class labels. We present a new end-to-end pai…
Phonetic Feedback for Speech Enhancement With and Without Parallel Speech Data
Peter Plantinga, Deblin Bagchi, Eric Fosler-Lussier
While deep learning systems have gained significant ground in speech enhancement research, these systems have yet to make use of the full potential of deep learning systems to prov…
Towards Real-time Mispronunciation Detection in Kids' Speech
Peter Plantinga, Eric Fosler-Lussier
Modern mispronunciation detection and diagnosis systems have seen significant gains in accuracy due to the introduction of deep learning. However, these systems have not been evalu…
Sequence-to-Set Semantic Tagging: End-to-End Multi-label Prediction using Neural Attention for Complex Query Reformulation and Automated Text Categorization
Manirupa Das, Juanxi Li, Eric Fosler-Lussier +5
Novel contexts may often arise in complex querying scenarios such as in evidence-based medicine (EBM) involving biomedical literature, that may not explicitly refer to entities or…
Learning to Answer Subjective, Specific Product-Related Queries using Customer Reviews by Adversarial Domain Adaptation
Manirupa Das, Zhen Wang, Evan Jaffe +3
Online customer reviews on large-scale e-commerce websites, represent a rich and varied source of opinion data, often providing subjective qualitative assessments of product usage…
Writing habits and telltale neighbors: analyzing clinical concept usage patterns with sublanguage embeddings
Denis Newman-Griffis, Eric Fosler-Lussier
Natural language processing techniques are being applied to increasingly diverse types of electronic health records, and can benefit from in-depth understanding of the distinguishi…
HARE: a Flexible Highlighting Annotator for Ranking and Exploration
Denis Newman-Griffis, Eric Fosler-Lussier
Exploration and analysis of potential data sources is a significant challenge in the application of NLP techniques to novel information domains. We describe HARE, a system for high…
Characterizing the impact of geometric properties of word embeddings on task performance
Brendan Whitaker, Denis Newman-Griffis, Aparajita Haldar +2
Analysis of word embedding properties to inform their use in downstream NLP tasks has largely been studied by assessing nearest neighbors. However, geometric properties of the cont…
An Exploration of Mimic Architectures for Residual Network Based Spectral Mapping
Peter Plantinga, Deblin Bagchi, Eric Fosler-Lussier
Spectral mapping uses a deep neural network (DNN) to map directly from noisy speech to clean speech. Our previous study found that the performance of spectral mapping improves grea…
Jointly Embedding Entities and Text with Distant Supervision
Denis Newman-Griffis, Albert M. Lai, Eric Fosler-Lussier
Learning representations for knowledge base entities and concepts is becoming increasingly important for NLP applications. However, recent entity embedding methods have relied on s…
Spectral feature mapping with mimic loss for robust speech recognition
Deblin Bagchi, Peter Plantinga, Adam Stiff +1
For the task of speech enhancement, local learning objectives are agnostic to phonetic structures helpful for speech recognition. We propose to add a global criterion to ensure de-…
Insights into Analogy Completion from the Biomedical Domain
Denis Newman-Griffis, Albert M Lai, Eric Fosler-Lussier
Analogy completion has been a popular task in recent years for evaluating the semantic properties of word embeddings, but the standard methodology makes a number of assumptions abo…