Search Results for author:
Found 45 papers, 18 papers with code
WeSep: A Scalable and Flexible Toolkit Towards Generalizable Target Speaker Extraction
Target speaker extraction (TSE) focuses on isolating the speech of a specific target speaker from overlapped multi-talker speech, which is a typical setup in the cocktail party problem.
Preference Alignment Improves Language Model-Based TTS
Recent advancements in text-to-speech (TTS) have shown that language model (LM)-based systems offer competitive performance to their counterparts.
Comparing Discrete and Continuous Space LLMs for Speech Recognition
This paper investigates discrete and continuous speech representations in Large Language Model (LLM)-based Automatic Speech Recognition (ASR), organizing them by feature continuity and training approach into four categories: supervised and unsupervised for both discrete and continuous types.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Gull: A Generative Multifunctional Audio Codec
We introduce Gull, a generative multifunctional audio codec.
Consistent and Relevant: Rethink the Query Embedding in General Sound Separation
In this paper, we present CaRE-SEP, a consistent and relevant embedding network for general sound separation to encourage a comprehensive reconsideration of query usage in audio separation.
Typing to Listen at the Cocktail Party: Text-Guided Target Speaker Extraction
We demonstrate that textual descriptions alone can effectively serve as cues for extraction, thus addressing privacy concerns and reducing dependency on voiceprints.
AutoPrep: An Automatic Preprocessing Framework for In-the-Wild Speech Data
To address this issue, we introduce an automatic in-the-wild speech data preprocessing framework (AutoPrep) in this paper, which is designed to enhance speech quality, generate speaker labels, and produce transcriptions automatically.
Leveraging In-the-Wild Data for Effective Self-Supervised Pretraining in Speaker Recognition
Current speaker recognition systems primarily rely on supervised approaches, constrained by the scale of labeled datasets.
Improved Factorized Neural Transducer Model For text-only Domain Adaptation
On out-of-domain datasets, IFNT shows relative WER(CER) improvements of up to 30. 2% over the standard neural Transducer with shallow fusion, and relative WER(CER) reductions ranging from 1. 1% to 2. 8% on test sets compared to the FNT model.
Complexity Scaling for Speech Denoising
A series of MPT networks present high performance covering a wide range of computational complexities on the DNS challenge dataset.
Ultra Dual-Path Compression For Joint Echo Cancellation And Noise Suppression
Echo cancellation and noise reduction are essential for full-duplex communication, yet most existing neural networks have high computational costs and are inflexible in tuning model complexity.
Bayes Risk Transducer: Transducer with Controllable Alignment Prediction
While the vanilla transducer does not have a prior preference for any of the valid paths, this work intends to enforce the preferred paths and achieve controllable alignment prediction.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
The Sound Demixing Challenge 2023 $\unicode{x2013}$ Music Demixing Track
We propose a formalization of the errors that can occur in the design of a training dataset for MSS systems and introduce two new datasets that simulate such errors: SDXDB23_LabelNoise and SDXDB23_Bleeding.
The Sound Demixing Challenge 2023 $\unicode{x2013}$ Cinematic Demixing Track
A significant source of this improvement was making the simulated data better match real cinematic audio, which we further investigate in detail.
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.
High Fidelity Speech Enhancement with Band-split RNN
Despite the rapid progress in speech enhancement (SE) research, enhancing the quality of desired speech in environments with strong noise and interfering speakers remains challenging.
Bayes risk CTC: Controllable CTC alignment in Sequence-to-Sequence tasks
Besides predicting the target sequence, a side product of CTC is to predict the alignment, which is the most probable input-long sequence that specifies a hard aligning relationship between the input and target units.
Music Source Separation with Band-split RNN
The performance of music source separation (MSS) models has been greatly improved in recent years thanks to the development of novel neural network architectures and training pipelines.
Ranked #3 on
Music Source Separation
on MUSDB18
(using extra training data)
FRA-RIR: Fast Random Approximation of the Image-source Method
The training of modern speech processing systems often requires a large amount of simulated room impulse response (RIR) data in order to allow the systems to generalize well in real-world, reverberant environments.
Diffsound: Discrete Diffusion Model for Text-to-sound Generation
In this study, we investigate generating sound conditioned on a text prompt and propose a novel text-to-sound generation framework that consists of a text encoder, a Vector Quantized Variational Autoencoder (VQ-VAE), a decoder, and a vocoder.
Ranked #14 on
Audio Generation
on AudioCaps
Automatic Prosody Annotation with Pre-Trained Text-Speech Model
Prosodic boundary plays an important role in text-to-speech synthesis (TTS) in terms of naturalness and readability.
LAE: Language-Aware Encoder for Monolingual and Multilingual ASR
Experiments conducted on Mandarin-English code-switched speech suggest that the proposed LAE is capable of discriminating different languages in frame-level and shows superior performance on both monolingual and multilingual ASR tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Audio-visual multi-channel speech separation, dereverberation and recognition
Despite the rapid advance of automatic speech recognition (ASR) technologies, accurate recognition of cocktail party speech characterised by the interference from overlapping speakers, background noise and room reverberation remains a highly challenging task to date.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Integrating Lattice-Free MMI into End-to-End Speech Recognition
However, the effectiveness and efficiency of the MBR-based methods are compromised: the MBR criterion is only used in system training, which creates a mismatch between training and decoding; the on-the-fly decoding process in MBR-based methods results in the need for pre-trained models and slow training speeds.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
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
Spectro-Temporal Deep Features for Disordered Speech Assessment and Recognition
Automatic recognition of disordered speech remains a highly challenging task to date.
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.
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
Improving Mandarin End-to-End Speech Recognition with Word N-gram Language Model
Then, the LM score of the hypothesis is obtained by intersecting the generated lattice with an external word N-gram LM.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Consistent Training and Decoding For End-to-end Speech Recognition Using Lattice-free MMI
Recently, End-to-End (E2E) frameworks have achieved remarkable results on various Automatic Speech Recognition (ASR) tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Low-bit Quantization of Recurrent Neural Network Language Models Using Alternating Direction Methods of Multipliers
Index Terms: Language models, Recurrent neural networks, Quantization, Alternating direction methods of multipliers.
Mixed Precision of Quantization of Transformer Language Models for Speech Recognition
Experiments conducted on Penn Treebank (PTB) and a Switchboard corpus trained LF-MMI TDNN system suggest the proposed mixed precision Transformer quantization techniques achieved model size compression ratios of up to 16 times over the full precision baseline with no recognition performance degradation.
Mixed Precision Low-bit Quantization of Neural Network Language Models for Speech Recognition
In order to overcome the difficulty in using gradient descent methods to directly estimate discrete quantized weights, alternating direction methods of multipliers (ADMM) are used to efficiently train quantized LMs.
Deconvolutional Networks on Graph Data
In this paper, we consider an inverse problem in graph learning domain -- ``given the graph representations smoothed by Graph Convolutional Network (GCN), how can we reconstruct the input graph signal?"
ASR-GLUE: A New Multi-task Benchmark for ASR-Robust Natural Language Understanding
%To facilitate the research on ASR-robust general language understanding, In this paper, we propose ASR-GLUE benchmark, a new collection of 6 different NLU tasks for evaluating the performance of models under ASR error across 3 different levels of background noise and 6 speakers with various voice characteristics.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Adversarial Data Augmentation for Disordered Speech Recognition
Automatic recognition of disordered speech remains a highly challenging task to date.
TeCANet: Temporal-Contextual Attention Network for Environment-Aware Speech Dereverberation
In this paper, we exploit the effective way to leverage contextual information to improve the speech dereverberation performance in real-world reverberant environments.
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
Audio-visual Multi-channel Recognition of Overlapped Speech
Automatic speech recognition (ASR) of overlapped speech remains a highly challenging task to date.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Bayesian x-vector: Bayesian Neural Network based x-vector System for Speaker Verification
Our experiment results indicate that the DNN x-vector system could benefit from BNNs especially when the mismatch problem is severe for evaluations using out-of-domain data.
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 #5 on
Audio-Visual Speech Recognition
on LRS2
Audio-Visual Speech Recognition
Automatic Speech Recognition (ASR)
+4
Adversarial Attacks on GMM i-vector based Speaker Verification Systems
Experiment results show that GMM i-vector systems are seriously vulnerable to adversarial attacks, and the crafted adversarial samples prove to be transferable and pose threats to neuralnetwork speaker embedding based systems (e. g. x-vector systems).
