Search Results for author:
Found 13 papers, 2 papers with code
Music Understanding LLaMA: Advancing Text-to-Music Generation with Question Answering and Captioning
To fill this gap, we present a methodology for generating question-answer pairs from existing audio captioning datasets and introduce the MusicQA Dataset designed for answering open-ended music-related questions.
Ranked #1 on
Music Question Answering
on MusicQA
A Hierarchical Speaker Representation Framework for One-shot Singing Voice Conversion
It is suspected that a single embedding vector may only capture averaged and coarse-grained speaker characteristics, which is insufficient for the SVC task.
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
Recent Progress in the CUHK Dysarthric Speech Recognition System
Despite the rapid progress of automatic speech recognition (ASR) technologies in the past few decades, recognition of disordered speech remains a highly challenging task to date.
Audio-Visual Speech Recognition
Automatic Speech Recognition
+4
Investigation of Data Augmentation Techniques for Disordered Speech Recognition
This paper investigates a set of data augmentation techniques for disordered speech recognition, including vocal tract length perturbation (VTLP), tempo perturbation and speed perturbation.
Spectro-Temporal Deep Features for Disordered Speech Assessment and Recognition
Automatic recognition of disordered speech remains a highly challenging task to date.
Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks
State-of-the-art automatic speech recognition (ASR) system development is data and computation intensive.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Adversarial Data Augmentation for Disordered Speech Recognition
Automatic recognition of disordered speech remains a highly challenging task to date.
Bayesian Transformer Language Models for Speech Recognition
Performance improvements were also obtained on a cross domain LM adaptation task requiring porting a Transformer LM trained on the Switchboard and Fisher data to a low-resource DementiaBank elderly speech corpus.
Bayesian Learning of LF-MMI Trained Time Delay Neural Networks for Speech Recognition
On a third cross domain adaptation task requiring rapidly porting a 1000 hour LibriSpeech data trained system to a small DementiaBank elderly speech corpus, the proposed Bayesian TDNN LF-MMI systems outperformed the baseline system using direct weight fine-tuning by up to 2. 5\% absolute WER reduction.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Audio-visual Multi-channel Integration and Recognition of Overlapped Speech
Automatic speech recognition (ASR) technologies have been significantly advanced in the past few decades.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks
Deep neural networks (DNNs) based automatic speech recognition (ASR) systems are often designed using expert knowledge and empirical evaluation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Audio-visual Recognition of Overlapped speech for the LRS2 dataset
Experiments on overlapped speech simulated from the LRS2 dataset suggest the proposed AVSR system outperformed the audio only baseline LF-MMI DNN system by up to 29. 98\% absolute in word error rate (WER) reduction, and produced recognition performance comparable to a more complex pipelined system.
Ranked #4 on
Audio-Visual Speech Recognition
on LRS2
Audio-Visual Speech Recognition
Automatic Speech Recognition (ASR)
+4
