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Found 10 papers, 0 papers with code
Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System
The goal is to decrease the cost and time needed to get an annotated corpus for the new language, while still having a large enough coverage of user requests.
Cross-lingual Transfer Learning for Japanese Named Entity Recognition
This work explores cross-lingual transfer learning (TL) for named entity recognition, focusing on bootstrapping Japanese from English.
Prosodic Representation Learning and Contextual Sampling for Neural Text-to-Speech
In Stage II, we propose a novel method to sample from this learnt prosodic distribution using the contextual information available in text.
A learned conditional prior for the VAE acoustic space of a TTS system
Many factors influence speech yielding different renditions of a given sentence.
Multi-Scale Spectrogram Modelling for Neural Text-to-Speech
We propose a novel Multi-Scale Spectrogram (MSS) modelling approach to synthesise speech with an improved coarse and fine-grained prosody.
CopyCat2: A Single Model for Multi-Speaker TTS and Many-to-Many Fine-Grained Prosody Transfer
In this paper, we present CopyCat2 (CC2), a novel model capable of: a) synthesizing speech with different speaker identities, b) generating speech with expressive and contextually appropriate prosody, and c) transferring prosody at fine-grained level between any pair of seen speakers.
Simple and Effective Multi-sentence TTS with Expressive and Coherent Prosody
In this paper, we examine simple extensions to a Transformer-based FastSpeech-like system, with the goal of improving prosody for multi-sentence TTS.
eCat: An End-to-End Model for Multi-Speaker TTS & Many-to-Many Fine-Grained Prosody Transfer
We show that eCat statistically significantly reduces the gap in naturalness between CopyCat2 and human recordings by an average of 46. 7% across 2 languages, 3 locales, and 7 speakers, along with better target-speaker similarity in FPT.
Controllable Emphasis with zero data for text-to-speech
We show that this technique significantly closes the gap to methods that require explicit recordings.
A Comparative Analysis of Pretrained Language Models for Text-to-Speech
In this study, we aim to address this gap by conducting a comparative analysis of different PLMs for two TTS tasks: prosody prediction and pause prediction.
