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From Generative to Discriminative: Lightweight Adaptation of SpeechLLMs for Multimodal Emotion Recognition
Hasindri Watawana, Sergio Burdisso, Esaú Villatoro-Tello +4
SpeechLLMs can recognise emotion by generating a label token, but this approach relies on the LLM's existing output representations. We compare this generative approach with a disc…
Closing the Speech-Text Gap with Limited Audio for Effective Domain Adaptation in LLM-Based ASR
Thibault Bañeras-Roux, Sergio Burdisso, Esaú Villatoro-Tello +9
Conventional end-to-end automatic speech recognition (ASR) systems rely on paired speech-text data for domain adaptation. Recent LLM-based ASR architectures connect a speech encode…
Abstract Compression of Conversational Audio Context for LLM-based ASR
Shashi Kumar, Esaú Villatoro-Tello, Sergio Burdisso +7
Standard LLM-based speech recognition systems process utterances in isolation, limiting their ability to leverage conversational context. Here we study whether multimodal context f…
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…
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…
Performance evaluation of SLAM-ASR: The Good, the Bad, the Ugly, and the Way Forward
Shashi Kumar, Iuliia Thorbecke, Sergio Burdisso +7
Recent research has demonstrated that training a linear connector between speech foundation encoders and large language models (LLMs) enables this architecture to achieve strong AS…