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Found 14 papers, 0 papers with code
Improved Text Emotion Prediction Using Combined Valence and Arousal Ordinal Classification
We thus redefine the emotion labeling problem by shifting it from a traditional classification model to an ordinal classification one, where discrete emotions are arranged in a sequential order according to their valence levels.
Low-Resource Cross-Domain Singing Voice Synthesis via Reduced Self-Supervised Speech Representations
In this paper, we propose a singing voice synthesis model, Karaoker-SSL, that is trained only on text and speech data as a typical multi-speaker acoustic model.
Predicting phoneme-level prosody latents using AR and flow-based Prior Networks for expressive speech synthesis
A large part of the expressive speech synthesis literature focuses on learning prosodic representations of the speech signal which are then modeled by a prior distribution during inference.
Learning utterance-level representations through token-level acoustic latents prediction for Expressive Speech Synthesis
We show that the fine-grained latent space also captures coarse-grained information, which is more evident as the dimension of latent space increases in order to capture diverse prosodic representations.
Generating Multilingual Gender-Ambiguous Text-to-Speech Voices
The gender of any voice user interface is a key element of its perceived identity.
Investigating Content-Aware Neural Text-To-Speech MOS Prediction Using Prosodic and Linguistic Features
In modern high-quality neural TTS systems, prosodic appropriateness with regard to the spoken content is a decisive factor for speech naturalness.
Cross-lingual Text-To-Speech with Flow-based Voice Conversion for Improved Pronunciation
When used in a cross-lingual setting, acoustic features are initially produced with a native speaker of the target language and then voice conversion is applied by the same model in order to convert these features to the target speaker's voice.
TASK AWARE MULTI-TASK LEARNING FOR SPEECH TO TEXT TASKS
In general, the direct Speech-to-text translation (ST) is jointly trained with Automatic Speech Recognition (ASR), and Machine Translation (MT) tasks.
Ranked #1 on
Speech-to-Text Translation
on MuST-C EN->DE
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Learning without Forgetting: Task Aware Multitask Learning for Multi-Modality Tasks
Inspired by these learning patterns in humans, we suggest a simple yet generic task aware framework to incorporate into existing joint learning strategies.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Faster Re-translation Using Non-Autoregressive Model For Simultaneous Neural Machine Translation
The current re-translation approaches are based on autoregressive sequence generation models (ReTA), which generate tar-get tokens in the (partial) translation sequentially.
VAE-PGN based Abstractive Model in Multi-stage Architecture for Text Summarization
This paper describes our submission to the TL;DR challenge.
Ensemble-Based Deep Reinforcement Learning for Chatbots
Experimental results using chitchat data reveal that (1) near human-like dialogue policies can be induced, (2) generalisation to unseen data is a difficult problem, and (3) training an ensemble of chatbot agents is essential for improved performance over using a single agent.
Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards
Training chatbots using the reinforcement learning paradigm is challenging due to high-dimensional states, infinite action spaces and the difficulty in specifying the reward function.
Self-Learning Architecture for Natural Language Generation
In this paper, we propose a self-learning architecture for generating natural language templates for conversational assistants.
