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Exploring Isolated Musical Notes as Pre-training Data for Predominant Instrument Recognition in Polyphonic Music
Lifan Zhong, Erica Cooper, Junichi Yamagishi +1
With the growing amount of musical data available, automatic instrument recognition, one of the essential problems in Music Information Retrieval (MIR), is drawing more and more at…
Range-Based Equal Error Rate for Spoof Localization
Lin Zhang, Xin Wang, Erica Cooper +2
Spoof localization, also called segment-level detection, is a crucial task that aims to locate spoofs in partially spoofed audio. The equal error rate (EER) is widely used to measu…
Speaker anonymization using orthogonal Householder neural network
Xiaoxiao Miao, Xin Wang, Erica Cooper +2
Speaker anonymization aims to conceal a speaker's identity while preserving content information in speech. Current mainstream neural-network speaker anonymization systems disentang…
Can Knowledge of End-to-End Text-to-Speech Models Improve Neural MIDI-to-Audio Synthesis Systems?
Xuan Shi, Erica Cooper, Xin Wang +2
With the similarity between music and speech synthesis from symbolic input and the rapid development of text-to-speech (TTS) techniques, it is worthwhile to explore ways to improve…
ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild
Xuechen Liu, Xin Wang, Md Sahidullah +8
Benchmarking initiatives support the meaningful comparison of competing solutions to prominent problems in speech and language processing. Successive benchmarking evaluations typic…
Analyzing Language-Independent Speaker Anonymization Framework under Unseen Conditions
Xiaoxiao Miao, Xin Wang, Erica Cooper +2
In our previous work, we proposed a language-independent speaker anonymization system based on self-supervised learning models. Although the system can anonymize speech data of any…
The VoiceMOS Challenge 2022
Wen-Chin Huang, Erica Cooper, Yu Tsao +3
We present the first edition of the VoiceMOS Challenge, a scientific event that aims to promote the study of automatic prediction of the mean opinion score (MOS) of synthetic speec…
Language-Independent Speaker Anonymization Approach using Self-Supervised Pre-Trained Models
Xiaoxiao Miao, Xin Wang, Erica Cooper +2
Speaker anonymization aims to protect the privacy of speakers while preserving spoken linguistic information from speech. Current mainstream neural network speaker anonymization sy…
Optimizing Tandem Speaker Verification and Anti-Spoofing Systems
Anssi Kanervisto, Ville Hautamäki, Tomi Kinnunen +1
As automatic speaker verification (ASV) systems are vulnerable to spoofing attacks, they are typically used in conjunction with spoofing countermeasure (CM) systems to improve secu…
On the Interplay Between Sparsity, Naturalness, Intelligibility, and Prosody in Speech Synthesis
Cheng-I Jeff Lai, Erica Cooper, Yang Zhang +8
Are end-to-end text-to-speech (TTS) models over-parametrized? To what extent can these models be pruned, and what happens to their synthesis capabilities? This work serves as a sta…
LDNet: Unified Listener Dependent Modeling in MOS Prediction for Synthetic Speech
Wen-Chin Huang, Erica Cooper, Junichi Yamagishi +1
An effective approach to automatically predict the subjective rating for synthetic speech is to train on a listening test dataset with human-annotated scores. Although each speech…
LaughNet: synthesizing laughter utterances from waveform silhouettes and a single laughter example
Hieu-Thi Luong, Junichi Yamagishi
Emotional and controllable speech synthesis is a topic that has received much attention. However, most studies focused on improving the expressiveness and controllability in the co…
Multi-Task Learning in Utterance-Level and Segmental-Level Spoof Detection
Lin Zhang, Xin Wang, Erica Cooper +1
In this paper, we provide a series of multi-tasking benchmarks for simultaneously detecting spoofing at the segmental and utterance levels in the PartialSpoof database. First, we p…
How do Voices from Past Speech Synthesis Challenges Compare Today?
Erica Cooper, Junichi Yamagishi
Shared challenges provide a venue for comparing systems trained on common data using a standardized evaluation, and they also provide an invaluable resource for researchers when th…
Preliminary study on using vector quantization latent spaces for TTS/VC systems with consistent performance
Hieu-Thi Luong, Junichi Yamagishi
Generally speaking, the main objective when training a neural speech synthesis system is to synthesize natural and expressive speech from the output layer of the neural network wit…
Visualizing Classifier Adjacency Relations: A Case Study in Speaker Verification and Voice Anti-Spoofing
Tomi Kinnunen, Andreas Nautsch, Md Sahidullah +6
Whether it be for results summarization, or the analysis of classifier fusion, some means to compare different classifiers can often provide illuminating insight into their behavio…
Text-to-Speech Synthesis Techniques for MIDI-to-Audio Synthesis
Erica Cooper, Xin Wang, Junichi Yamagishi
Speech synthesis and music audio generation from symbolic input differ in many aspects but share some similarities. In this study, we investigate how text-to-speech synthesis techn…
Pretraining Strategies, Waveform Model Choice, and Acoustic Configurations for Multi-Speaker End-to-End Speech Synthesis
Erica Cooper, Xin Wang, Yi Zhao +2
We explore pretraining strategies including choice of base corpus with the aim of choosing the best strategy for zero-shot multi-speaker end-to-end synthesis. We also examine choic…
Denoising-and-Dereverberation Hierarchical Neural Vocoder for Robust Waveform Generation
Yang Ai, Haoyu Li, Xin Wang +2
This paper presents a denoising and dereverberation hierarchical neural vocoder (DNR-HiNet) to convert noisy and reverberant acoustic features into a clean speech waveform. We impl…
Reverberation Modeling for Source-Filter-based Neural Vocoder
Yang Ai, Xin Wang, Junichi Yamagishi +1
This paper presents a reverberation module for source-filter-based neural vocoders that improves the performance of reverberant effect modeling. This module uses the output wavefor…
Transformation of low-quality device-recorded speech to high-quality speech using improved SEGAN model
Seyyed Saeed Sarfjoo, Xin Wang, Gustav Eje Henter +3
Nowadays vast amounts of speech data are recorded from low-quality recorder devices such as smartphones, tablets, laptops, and medium-quality microphones. The objective of this res…
MOSNet: Deep Learning based Objective Assessment for Voice Conversion
Chen-Chou Lo, Szu-Wei Fu, Wen-Chin Huang +4
Existing objective evaluation metrics for voice conversion (VC) are not always correlated with human perception. Therefore, training VC models with such criteria may not effectivel…
Introduction to Voice Presentation Attack Detection and Recent Advances
Md Sahidullah, Hector Delgado, Massimiliano Todisco +4
Over the past few years significant progress has been made in the field of presentation attack detection (PAD) for automatic speaker recognition (ASV). This includes the developmen…
Transforming acoustic characteristics to deceive playback spoofing countermeasures of speaker verification systems
Fuming Fang, Junichi Yamagishi, Isao Echizen +2
Automatic speaker verification (ASV) systems use a playback detector to filter out playback attacks and ensure verification reliability. Since current playback detection models are…