Search Results for author:
Found 25 papers, 6 papers with code
Optimization of DNN-based speaker verification model through efficient quantization technique
By analyzing performance changes and model size reductions in each layer of a pre-trained speaker verification model, we have effectively minimized performance degradation while significantly reducing the model size.
Speaker-Independent Acoustic-to-Articulatory Inversion through Multi-Channel Attention Discriminator
We present a novel speaker-independent acoustic-to-articulatory inversion (AAI) model, overcoming the limitations observed in conventional AAI models that rely on acoustic features derived from restricted datasets.
Speak in the Scene: Diffusion-based Acoustic Scene Transfer toward Immersive Speech Generation
This paper introduces a novel task in generative speech processing, Acoustic Scene Transfer (AST), which aims to transfer acoustic scenes of speech signals to diverse environments.
Enhanced Deep Speech Separation in Clustered Ad Hoc Distributed Microphone Environments
Ad-hoc distributed microphone environments, where microphone locations and numbers are unpredictable, present a challenge to traditional deep learning models, which typically require fixed architectures.
Style Modeling for Multi-Speaker Articulation-to-Speech
In this paper, we propose a neural articulation-to-speech (ATS) framework that synthesizes high-quality speech from articulatory signal in a multi-speaker situation.
Self-supervised Complex Network for Machine Sound Anomaly Detection
In this paper, we propose an anomaly detection algorithm for machine sounds with a deep complex network trained by self-supervision.
BrainTalker: Low-Resource Brain-to-Speech Synthesis with Transfer Learning using Wav2Vec 2.0
Specifically, we train an encoder module to map ECoG signals to latent embeddings that match Wav2Vec 2. 0 representations of the corresponding spoken speech.
C2C: Cough to COVID-19 Detection in BHI 2023 Data Challenge
This report describes our submission to BHI 2023 Data Competition: Sensor challenge.
Pruning Self-Attention for Zero-Shot Multi-Speaker Text-to-Speech
To this end, the baseline TTS model needs to be amply generalized to out-of-domain data (i. e., target speaker's speech).
Contrastive Learning based Deep Latent Masking for Music Source Separation
Recent studies on music source separation have extended their applicability to generic audio signals.
Ranked #13 on
Music Source Separation
on MUSDB18
MF-PAM: Accurate Pitch Estimation through Periodicity Analysis and Multi-level Feature Fusion
We introduce Multi-level feature Fusion-based Periodicity Analysis Model (MF-PAM), a novel deep learning-based pitch estimation model that accurately estimates pitch trajectory in noisy and reverberant acoustic environments.
Feature Normalization for Fine-tuning Self-Supervised Models in Speech Enhancement
Large, pre-trained representation models trained using self-supervised learning have gained popularity in various fields of machine learning because they are able to extract high-quality salient features from input data.
HD-DEMUCS: General Speech Restoration with Heterogeneous Decoders
This paper introduces an end-to-end neural speech restoration model, HD-DEMUCS, demonstrating efficacy across multiple distortion environments.
Learning Audio-Text Agreement for Open-vocabulary Keyword Spotting
In this paper, we propose a novel end-to-end user-defined keyword spotting method that utilizes linguistically corresponding patterns between speech and text sequences.
ReCAB-VAE: Gumbel-Softmax Variational Inference Based on Analytic Divergence
In this work, we present a relaxed categorical analytic bound (ReCAB), a novel divergence-like metric which corresponds to the upper bound of the Kullback-Leibler divergence (KLD) of a relaxed categorical distribution.
Phase Continuity: Learning Derivatives of Phase Spectrum for Speech Enhancement
Modern neural speech enhancement models usually include various forms of phase information in their training loss terms, either explicitly or implicitly.
Facetron: A Multi-speaker Face-to-Speech Model based on Cross-modal Latent Representations
In this paper, we propose a multi-speaker face-to-speech waveform generation model that also works for unseen speaker conditions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Looking into Your Speech: Learning Cross-modal Affinity for Audio-visual Speech Separation
In this paper, we address the problem of separating individual speech signals from videos using audio-visual neural processing.
MIRNet: Learning multiple identities representations in overlapped speech
Many approaches can derive information about a single speaker's identity from the speech by learning to recognize consistent characteristics of acoustic parameters.
FaceFilter: Audio-visual speech separation using still images
The objective of this paper is to separate a target speaker's speech from a mixture of two speakers using a deep audio-visual speech separation network.
Improving LPCNet-based Text-to-Speech with Linear Prediction-structured Mixture Density Network
In this paper, we propose an improved LPCNet vocoder using a linear prediction (LP)-structured mixture density network (MDN).
Effective parameter estimation methods for an ExcitNet model in generative text-to-speech systems
In this paper, we propose a high-quality generative text-to-speech (TTS) system using an effective spectrum and excitation estimation method.
ExcitNet vocoder: A neural excitation model for parametric speech synthesis systems
Conventional WaveNet-based neural vocoding systems significantly improve the perceptual quality of synthesized speech by statistically generating a time sequence of speech waveforms through an auto-regressive framework.
Speaker-adaptive neural vocoders for parametric speech synthesis systems
To generate more natural speech signals with the constraint of limited training data, we propose a speaker adaptation task with an effective variation of neural vocoding models.
Perfect match: Improved cross-modal embeddings for audio-visual synchronisation
This paper proposes a new strategy for learning powerful cross-modal embeddings for audio-to-video synchronization.
