Search Results for author:
Found 22 papers, 3 papers with code
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.
Hyper-parameter Adaptation of Conformer ASR Systems for Elderly and Dysarthric Speech Recognition
Automatic recognition of disordered and elderly speech remains highly challenging tasks to date due to data scarcity.
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.
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.
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.
Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detection
Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care and to delay further progression.
Exploring linguistic feature and model combination for speech recognition based automatic AD detection
Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care and delay progression.
Confidence Score Based Conformer Speaker Adaptation for Speech Recognition
A key challenge for automatic speech recognition (ASR) systems is to model the speaker level variability.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection
Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care to delay further progression.
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
A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation
Non-autoregressive (NAR) models simultaneously generate multiple outputs in a sequence, which significantly reduces the inference speed at the cost of accuracy drop compared to autoregressive baselines.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
An Exploration of Self-Supervised Pretrained Representations for End-to-End Speech Recognition
We select several pretrained speech representations and present the experimental results on various open-source and publicly available corpora for E2E-ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Streaming End-to-End ASR based on Blockwise Non-Autoregressive Models
Non-autoregressive (NAR) modeling has gained more and more attention in speech processing.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Toward Streaming ASR with Non-Autoregressive Insertion-based Model
We propose a system to concatenate audio segmentation and non-autoregressive ASR to realize high accuracy and low RTF ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
x-vectors meet emotions: A study on dependencies between emotion and speaker recognition
Then, we show the effect of emotion on speaker recognition.
