13 accepted papers
Existing speech tokenizers typically assign a fixed number of tokens per second, regardless of the varying information density or temporal fluctuations in the speech signal. This uniform token allocation mismatches the intrinsic structure of speech, where information is distributed unevenly over tim
In this paper, a multi-scale feature fusion paradigm is proposed to fully exploit the power of the pre-trained models for text-independent speaker verification. It contains a front-end feature extractor and an enhanced ECAPA-TDNN backend in a cascade manner. The feature extractor incorporates local…
In this paper, we propose a noise-invariant speaker representation learning (SRL) approach by aligning noisy-clean speech pairs at both the feature and embedding levels for model training. Specifically, we first construct noisy-clean pairs using data augmentation during training. The noisy features…
Cross-lingual voice conversion (XVC) is a technology that modifies speaker identity while preserving linguistic content in scenarios where the source and target speakers use different languages. Previous non-parallel disentanglement-based methods face severe training-testing inconsistency issues in…
Recent advances in automatic speech recognition (ASR) technology have boosted the viability of fully automated Alzheimer’s disease (AD) detection via ASR transcripts. However, there is a lack of understanding of how ASR errors affect the performance of AD detection. This paper addresses that gap. Fi…
This paper proposes an Incremental Disentanglement-based Environment-Aware zero-shot text-to-speech (TTS) method, dubbed IDEA-TTS, that can synthesize speech for unseen speakers while preserving the acoustic characteristics of a given environment reference speech. IDEA-TTS adopts VITS as the TTS bac…
It was recently revealed that using features extracted from pre-trained models can achieve much better performance than using conventional hand-crafted acoustic features for spoofing speech detection. In this paper, we therefore enhance the features from pre-trained model based on recursive learning…
Conversational speech synthesis aims to synthesize speech of an individual speaker based on history conversation. However, most studies in conversational speech synthesis only focus on the synthesis performance of the current speaker’s turn and neglect the temporal relationship between turns of inte…
This paper presents a novel speech phase prediction model which predicts wrapped phase spectra directly from amplitude spectra by neural networks. The proposed model is a cascade of a residual convolutional network and a parallel estimation architecture. The parallel estimation architecture is compo…
This paper studies the task of speech reconstruction from ultrasound tongue images and optical lip videos recorded in a silent speaking mode, where people only activate their intra-oral and extra-oral articulators without producing sound. This task falls under the umbrella of articulatory-to-acousti…
Lip-to-Speech (Lip2Speech) synthesis, which predicts corresponding speech from talking face images, has witnessed significant progress with various models and training strategies in a series of independent studies. However, existing studies can not achieve voice control under zero-shot condition, be…
This paper presents a spectral enhancement method to improve the quality of speech reconstructed by neural waveform generators with low-bit quantization. At training stage, this method builds a multiple-target DNN, which predicts log amplitude spectra of natural high-bit waveforms together with the…
This paper presents a SampleRNN-based neural vocoder for statistical parametric speech synthesis. This method utilizes a conditional SampleRNN model composed of a hierarchical structure of GRU layers and feed-forward layers to capture long-span dependencies between acoustic features and waveform seq…