Search Results for author:
Found 17 papers, 2 papers with code
WavLLM: Towards Robust and Adaptive Speech Large Language Model
In this work, we introduce WavLLM, a robust and adaptive speech large language model with dual encoders, and a prompt-aware LoRA weight adapter, optimized by a two-stage curriculum learning approach.
Boosting Large Language Model for Speech Synthesis: An Empirical Study
In this paper, we conduct a comprehensive empirical exploration of boosting LLMs with the ability to generate speech, by combining pre-trained LLM LLaMA/OPT and text-to-speech synthesis model VALL-E. We compare three integration methods between LLMs and speech synthesis models, including directly fine-tuned LLMs, superposed layers of LLMs and VALL-E, and coupled LLMs and VALL-E using LLMs as a powerful text encoder.
Towards Automatic Data Augmentation for Disordered Speech Recognition
Automatic recognition of disordered speech remains a highly challenging task to date due to data scarcity.
Audio-visual End-to-end Multi-channel Speech Separation, Dereverberation and Recognition
Accurate recognition of cocktail party speech containing overlapping speakers, noise and reverberation remains a highly challenging task to date.
Factorised Speaker-environment Adaptive Training of Conformer Speech Recognition Systems
Rich sources of variability in natural speech present significant challenges to current data intensive speech recognition technologies.
Have Large Language Models Developed a Personality?: Applicability of Self-Assessment Tests in Measuring Personality in LLMs
In this paper, we show that we do not yet have the right tools to measure personality in language models.
Use of Speech Impairment Severity for Dysarthric Speech Recognition
A key challenge in dysarthric speech recognition is the speaker-level diversity attributed to both speaker-identity associated factors such as gender, and speech impairment severity.
Exploring Self-supervised Pre-trained ASR Models For Dysarthric and Elderly Speech Recognition
Experiments conducted on the UASpeech dysarthric and DementiaBank Pitt elderly speech corpora suggest TDNN and Conformer ASR systems integrated domain adapted wav2vec2. 0 models consistently outperform the standalone wav2vec2. 0 models by statistically significant WER reductions of 8. 22% and 3. 43% absolute (26. 71% and 15. 88% relative) on the two tasks respectively.
Confidence Score Based Speaker Adaptation of Conformer Speech Recognition Systems
Practical application of unsupervised model-based speaker adaptation techniques to data intensive end-to-end ASR systems is hindered by the scarcity of speaker-level data and performance sensitivity to transcription errors.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Adversarial Data Augmentation Using VAE-GAN for Disordered Speech Recognition
After LHUC speaker adaptation, the best system using VAE-GAN based augmentation produced an overall WER of 27. 78% on the UASpeech test set of 16 dysarthric speakers, and the lowest published WER of 57. 31% on the subset of speakers with "Very Low" intelligibility.
Two-pass Decoding and Cross-adaptation Based System Combination of End-to-end Conformer and Hybrid TDNN ASR Systems
Fundamental modelling differences between hybrid and end-to-end (E2E) automatic speech recognition (ASR) systems create large diversity and complementarity among them.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Exploiting Cross-domain And Cross-Lingual Ultrasound Tongue Imaging Features For Elderly And Dysarthric Speech Recognition
Articulatory features are inherently invariant to acoustic signal distortion and have been successfully incorporated into automatic speech recognition (ASR) systems designed for normal speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition
Despite the rapid progress of automatic speech recognition (ASR) technologies targeting normal speech, accurate recognition of dysarthric and elderly speech remains highly challenging tasks to date.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
On-the-Fly Feature Based Rapid Speaker Adaptation for Dysarthric and Elderly Speech Recognition
Accurate recognition of dysarthric and elderly speech remain challenging tasks to date.
Exploiting Cross Domain Acoustic-to-articulatory Inverted Features For Disordered Speech Recognition
Articulatory features are inherently invariant to acoustic signal distortion and have been successfully incorporated into automatic speech recognition (ASR) systems for normal speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Speaker Adaptation Using Spectro-Temporal Deep Features for Dysarthric and Elderly Speech Recognition
Motivated by the spectro-temporal level differences between dysarthric, elderly and normal speech that systematically manifest in articulatory imprecision, decreased volume and clarity, slower speaking rates and increased dysfluencies, novel spectrotemporal subspace basis deep embedding features derived using SVD speech spectrum decomposition are proposed in this paper to facilitate auxiliary feature based speaker adaptation of state-of-the-art hybrid DNN/TDNN and end-to-end Conformer speech recognition systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
GraphGT: Machine Learning Datasets for Graph Generation and Transformation
Graph generation, which learns from known graphs and discovers novel graphs, has great potential in numerous research topics like drug design and mobility synthesis and is one of the fastest-growing domains recently due to its promise for discovering new knowledge.
