Search Results for author:
Found 13 papers, 4 papers with code
Advancing Large Language Models to Capture Varied Speaking Styles and Respond Properly in Spoken Conversations
When using text-only LLMs to model spoken dialogue, text-only LLMs cannot give different responses based on the speaking style of the current turn.
SpeechDPR: End-to-End Spoken Passage Retrieval for Open-Domain Spoken Question Answering
However, the real-world problem of Open-domain SQA (openSQA), in which the machine needs to first retrieve passages that possibly contain the answer from a spoken archive in addition, was never considered.
Towards ASR Robust Spoken Language Understanding Through In-Context Learning With Word Confusion Networks
In the realm of spoken language understanding (SLU), numerous natural language understanding (NLU) methodologies have been adapted by supplying large language models (LLMs) with transcribed speech instead of conventional written text.
Paralinguistics-Enhanced Large Language Modeling of Spoken Dialogue
Specifically, our framework serializes tasks in the order of current paralinguistic attribute prediction, response paralinguistic attribute prediction, and response text generation with autoregressive conditioning.
GSQA: An End-to-End Model for Generative Spoken Question Answering
Furthermore, the GSQA model has only been fine-tuned on the spoken extractive QA dataset.
Improving Textless Spoken Language Understanding with Discrete Units as Intermediate Target
However, the absence of intermediate targets and training guidance for textless SLU often results in suboptimal performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Introducing Semantics into Speech Encoders
Recent studies find existing self-supervised speech encoders contain primarily acoustic rather than semantic information.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+10
On the Utility of Self-supervised Models for Prosody-related Tasks
We find that 13 of the 15 SSL models outperformed the baseline on all the prosody-related tasks.
Listen, Adapt, Better WER: Source-free Single-utterance Test-time Adaptation for Automatic Speech Recognition
Although deep learning-based end-to-end Automatic Speech Recognition (ASR) has shown remarkable performance in recent years, it suffers severe performance regression on test samples drawn from different data distributions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken Question Answering
We empirically showed that DUAL yields results comparable to those obtained by cascading ASR and text QA model and robust to real-world data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Context-gloss Augmentation for Improving Word Sense Disambiguation
Deep-learning techniques using BERT have achieved very promising results in the field and different methods have been proposed to integrate structured knowledge to enhance performance.
Analyzing the Robustness of Unsupervised Speech Recognition
In this work, we further analyze the training robustness of unsupervised ASR on the domain mismatch scenarios in which the domains of unpaired speech and text are different.
SUPERB: Speech processing Universal PERformance Benchmark
SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data.
