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Found 24 papers, 4 papers with code
On permutation-invariant neural networks
From these observations, we show that Deep Sets, one of the well-known permutation-invariant neural networks, can be generalized in the sense of a quasi-arithmetic mean.
Frame-Wise Breath Detection with Self-Training: An Exploration of Enhancing Breath Naturalness in Text-to-Speech
Developing Text-to-Speech (TTS) systems that can synthesize natural breath is essential for human-like voice agents but requires extensive manual annotation of breath positions in training data.
StyleCap: Automatic Speaking-Style Captioning from Speech Based on Speech and Language Self-supervised Learning Models
The experimental results demonstrate that our StyleCap leveraging richer LLMs for the text decoder, speech self-supervised learning (SSL) features, and sentence rephrasing augmentation improves the accuracy and diversity of generated speaking-style captions.
Outfit Completion via Conditional Set Transformation
In this paper, we formulate the outfit completion problem as a set retrieval task and propose a novel framework for solving this problem.
Virtual Human Generative Model: Masked Modeling Approach for Learning Human Characteristics
In this paper, we propose Virtual Human Generative Model (VHGM), a machine learning model for estimating attributes about healthcare, lifestyles, and personalities.
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.
Duration-aware pause insertion using pre-trained language model for multi-speaker text-to-speech
We also leverage duration-aware pause insertion for more natural multi-speaker TTS.
Multi-Task Adversarial Training Algorithm for Multi-Speaker Neural Text-to-Speech
A conventional generative adversarial network (GAN)-based training algorithm significantly improves the quality of synthetic speech by reducing the statistical difference between natural and synthetic speech.
Human-in-the-loop Speaker Adaptation for DNN-based Multi-speaker TTS
With a conventional speaker-adaptation method, a target speaker's embedding vector is extracted from his/her reference speech using a speaker encoder trained on a speaker-discriminative task.
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.
SHIFT15M: Fashion-specific dataset for set-to-set matching with several distribution shifts
This paper addresses the problem of set-to-set matching, which involves matching two different sets of items based on some criteria, especially in the case of high-dimensional items like images.
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.
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.
Exchangeable deep neural networks for set-to-set matching and learning
Matching two different sets of items, called heterogeneous set-to-set matching problem, has recently received attention as a promising problem.
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
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.
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.
