1 citations · 3 across the 20 of their papers we have counts for
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Spoken Conversational Agents with Large Language Models
Chao-Han Huck Yang, Andreas Stolcke, Larry Heck
Spoken conversational agents are converging toward voice-native LLMs. This tutorial distills the path from cascaded ASR/NLU to end-to-end, retrieval-and vision-grounded systems. We…
Slot Filling as a Reasoning Task for SpeechLLMs
Kadri Hacioglu, Manjunath K E, Andreas Stolcke
We propose integration of reasoning into speech large language models (speechLLMs) for the end-to-end slot-filling task. Inspired by the recent development of reasoning LLMs, we us…
SpeechLLMs for Large-scale Contextualized Zero-shot Slot Filling
Kadri Hacioglu, Manjunath K E, Andreas Stolcke
Slot filling is a crucial subtask in spoken language understanding (SLU), traditionally implemented as a cascade of speech recognition followed by one or more natural language unde…
TokenVerse++: Towards Flexible Multitask Learning with Dynamic Task Activation
Shashi Kumar, Srikanth Madikeri, Esaú Villatoro-Tello +8
Token-based multitasking frameworks like TokenVerse require all training utterances to have labels for all tasks, hindering their ability to leverage partially annotated datasets a…
Unifying Streaming and Non-streaming Zipformer-based ASR
Bidisha Sharma, Karthik Pandia Durai, Shankar Venkatesan +4
There has been increasing interest in unifying streaming and non-streaming automatic speech recognition (ASR) models to reduce development, training, and deployment costs. We prese…
Better Pseudo-labeling with Multi-ASR Fusion and Error Correction by SpeechLLM
Jeena Prakash, Blessingh Kumar, Kadri Hacioglu +5
Automatic speech recognition (ASR) models rely on high-quality transcribed data for effective training. Generating pseudo-labels for large unlabeled audio datasets often relies on…
Better Semi-supervised Learning for Multi-domain ASR Through Incremental Retraining and Data Filtering
Andres Carofilis, Pradeep Rangappa, Srikanth Madikeri +10
Fine-tuning pretrained ASR models for specific domains is challenging when labeled data is scarce. But unlabeled audio and labeled data from related domains are often available. We…
Efficient Data Selection for Domain Adaptation of ASR Using Pseudo-Labels and Multi-Stage Filtering
Pradeep Rangappa, Andres Carofilis, Jeena Prakash +10
Fine-tuning pretrained ASR models for specific domains is challenging for small organizations with limited labeled data and computational resources. Here, we explore different data…
Improving endpoint detection in end-to-end streaming ASR for conversational speech
Anandh C, Karthik Pandia Durai, Jeena Prakash +6
ASR endpointing (EP) plays a major role in delivering a good user experience in products supporting human or artificial agents in human-human/machine conversations. Transducer-base…