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Found 25 papers, 6 papers with code
How Generative Spoken Language Modeling Encodes Noisy Speech: Investigation from Phonetics to Syntactics
We examine the speech modeling potential of generative spoken language modeling (GSLM), which involves using learned symbols derived from data rather than phonemes for speech analysis and synthesis.
CALLS: Japanese Empathetic Dialogue Speech Corpus of Complaint Handling and Attentive Listening in Customer Center
We present CALLS, a Japanese speech corpus that considers phone calls in a customer center as a new domain of empathetic spoken dialogue.
ChatGPT-EDSS: Empathetic Dialogue Speech Synthesis Trained from ChatGPT-derived Context Word Embeddings
We focus on ChatGPT's reading comprehension and introduce it to EDSS, a task of synthesizing speech that can empathize with the interlocutor's emotion.
Learning to Speak from Text: Zero-Shot Multilingual Text-to-Speech with Unsupervised Text Pretraining
While neural text-to-speech (TTS) has achieved human-like natural synthetic speech, multilingual TTS systems are limited to resource-rich languages due to the need for paired text and studio-quality audio data.
JaCappella Corpus: A Japanese a Cappella Vocal Ensemble Corpus
These songs were arranged from out-of-copyright Japanese children's songs and have six voice parts (lead vocal, soprano, alto, tenor, bass, and vocal percussion).
Ranked #1 on
Vocal ensemble separation
on jaCappella
Empirical Study Incorporating Linguistic Knowledge on Filled Pauses for Personalized Spontaneous Speech Synthesis
We present a comprehensive empirical study for personalized spontaneous speech synthesis on the basis of linguistic knowledge.
Acoustic Modeling for End-to-End Empathetic Dialogue Speech Synthesis Using Linguistic and Prosodic Contexts of Dialogue History
To train the empathetic DSS model effectively, we investigate 1) a self-supervised learning model pretrained with large speech corpora, 2) a style-guided training using a prosody embedding of the current utterance to be predicted by the dialogue context embedding, 3) a cross-modal attention to combine text and speech modalities, and 4) a sentence-wise embedding to achieve fine-grained prosody modeling rather than utterance-wise modeling.
STUDIES: Corpus of Japanese Empathetic Dialogue Speech Towards Friendly Voice Agent
We describe our methodology to construct an empathetic dialogue speech corpus and report the analysis results of the STUDIES corpus.
ESPnet2-TTS: Extending the Edge of TTS Research
This paper describes ESPnet2-TTS, an end-to-end text-to-speech (E2E-TTS) toolkit.
Low-Latency Incremental Text-to-Speech Synthesis with Distilled Context Prediction Network
Although this method achieves comparable speech quality to that of a method that waits for the future context, it entails a huge amount of processing for sampling from the language model at each time step.
Onoma-to-wave: Environmental sound synthesis from onomatopoeic words
We also propose a method of environmental sound synthesis using onomatopoeic words and sound event labels.
Sound Audio and Speech Processing
HumanACGAN: conditional generative adversarial network with human-based auxiliary classifier and its evaluation in phoneme perception
A DNN-based generator is trained using a human-based discriminator, i. e., humans' perceptual evaluations, instead of the GAN's DNN-based discriminator.
RWCP-SSD-Onomatopoeia: Onomatopoeic Word Dataset for Environmental Sound Synthesis
We believe that using onomatopoeic words will enable us to control the fine time-frequency structure of synthesized sounds.
Sound Audio and Speech Processing
SMASH Corpus: A Spontaneous Speech Corpus Recording Third-person Audio Commentaries on Gameplay
Developing a spontaneous speech corpus would be beneficial for spoken language processing and understanding.
DNN-based Speech Synthesis Using Abundant Tags of Spontaneous Speech Corpus
In this paper, we investigate the effectiveness of using rich annotations in deep neural network (DNN)-based statistical speech synthesis.
HumanGAN: generative adversarial network with human-based discriminator and its evaluation in speech perception modeling
To model the human-acceptable distribution, we formulate a backpropagation-based generator training algorithm by regarding human perception as a black-boxed discriminator.
V2S attack: building DNN-based voice conversion from automatic speaker verification
The experimental evaluation compares converted voices between the proposed method that does not use the targeted speaker's voice data and the standard VC that uses the data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
DNN-based Speaker Embedding Using Subjective Inter-speaker Similarity for Multi-speaker Modeling in Speech Synthesis
Although conventional DNN-based speaker embedding such as a $d$-vector can be applied to multi-speaker modeling in speech synthesis, it does not correlate with the subjective inter-speaker similarity and is not necessarily appropriate speaker representation for open speakers whose speech utterances are not included in the training data.
Generative Moment Matching Network-based Random Modulation Post-filter for DNN-based Singing Voice Synthesis and Neural Double-tracking
To address this problem, we use a GMMN to model the variation of the modulation spectrum of the pitch contour of natural singing voices and add a randomized inter-utterance variation to the pitch contour generated by conventional DNN-based singing voice synthesis.
Phase reconstruction from amplitude spectrograms based on von-Mises-distribution deep neural network
This paper presents a deep neural network (DNN)-based phase reconstruction from amplitude spectrograms.
Sound Audio and Speech Processing
JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis
Thanks to improvements in machine learning techniques including deep learning, a free large-scale speech corpus that can be shared between academic institutions and commercial companies has an important role.
Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks
In the proposed framework incorporating the GANs, the discriminator is trained to distinguish natural and generated speech parameters, while the acoustic models are trained to minimize the weighted sum of the conventional minimum generation loss and an adversarial loss for deceiving the discriminator.
Sampling-based speech parameter generation using moment-matching networks
To give synthetic speech natural inter-utterance variation, this paper builds DNN acoustic models that make it possible to randomly sample speech parameters.
Voice Conversion Using Sequence-to-Sequence Learning of Context Posterior Probabilities
Conventional VC using shared context posterior probabilities predicts target speech parameters from the context posterior probabilities estimated from the source speech parameters.
