21 citations · 154 across the 60 of their papers we have counts for
13 papers · 1 filter
GLoRIA: Gated Low-Rank Interpretable Adaptation for Dialectal ASR
Pouya Mehralian, Melissa Farasyn, Anne Breitbarth +2
Automatic Speech Recognition (ASR) in dialect-heavy settings remains challenging due to strong regional variation and limited labeled data. We propose GLoRIA, a parameter-efficient…
SSVD: Structured SVD for Parameter-Efficient Fine-Tuning and Benchmarking under Domain Shift in ASR
Pu Wang, Shinji Watanabe, Hugo Van hamme
Parameter-efficient fine-tuning (PEFT) has emerged as a scalable solution for adapting large foundation models. While low-rank adaptation (LoRA) is widely used in speech applicatio…
Refining Transcripts With TV Subtitles by Prompt-Based Weakly Supervised Training of ASR
Xinnian Zhao, Hugo Van Hamme
This study proposes a novel approach to using TV subtitles within a weakly supervised (WS) Automatic Speech Recognition (ASR) framework. Although TV subtitles are readily available…
MSNER: A Multilingual Speech Dataset for Named Entity Recognition
Quentin Meeus, Marie-Francine Moens, Hugo Van hamme
While extensively explored in text-based tasks, Named Entity Recognition (NER) remains largely neglected in spoken language understanding. Existing resources are limited to a singl…
Multitask Learning for Low Resource Spoken Language Understanding
Quentin Meeus, Marie-Francine Moens, Hugo Van hamme
We explore the benefits that multitask learning offer to speech processing as we train models on dual objectives with automatic speech recognition and intent classification or sent…
Bidirectional Representations for Low Resource Spoken Language Understanding
Quentin Meeus, Marie-Francine Moens, Hugo Van hamme
Most spoken language understanding systems use a pipeline approach composed of an automatic speech recognition interface and a natural language understanding module. This approach…
Bottleneck Low-rank Transformers for Low-resource Spoken Language Understanding
Pu Wang, Hugo Van hamme
End-to-end spoken language understanding (SLU) systems benefit from pretraining on large corpora, followed by fine-tuning on application-specific data. The resulting models are too…
On the long-term learning ability of LSTM LMs
Wim Boes, Robbe Van Rompaey, Lyan Verwimp +3
We inspect the long-term learning ability of Long Short-Term Memory language models (LSTM LMs) by evaluating a contextual extension based on the Continuous Bag-of-Words (CBOW) mode…
Effective weakly supervised semantic frame induction using expression sharing in hierarchical hidden Markov models
Janneke van de Loo, Jort F. Gemmeke, Guy De Pauw +3
We present a framework for the induction of semantic frames from utterances in the context of an adaptive command-and-control interface. The system is trained on an individual user…
Information-Weighted Neural Cache Language Models for ASR
Lyan Verwimp, Joris Pelemans, Hugo Van hamme +1
Neural cache language models (LMs) extend the idea of regular cache language models by making the cache probability dependent on the similarity between the current context and the…
State Gradients for RNN Memory Analysis
Lyan Verwimp, Hugo Van hamme, Vincent Renkens +1
We present a framework for analyzing what the state in RNNs remembers from its input embeddings. Our approach is inspired by backpropagation, in the sense that we compute the gradi…
Language Models of Spoken Dutch
Lyan Verwimp, Joris Pelemans, Marieke Lycke +2
In Flanders, all TV shows are subtitled. However, the process of subtitling is a very time-consuming one and can be sped up by providing the output of a speech recognizer run on th…
Character-Word LSTM Language Models
Lyan Verwimp, Joris Pelemans, Hugo Van hamme +1
We present a Character-Word Long Short-Term Memory Language Model which both reduces the perplexity with respect to a baseline word-level language model and reduces the number of p…