5 papers
Simple and Effective Multi-sentence TTS with Expressive and Coherent Prosody
Peter Makarov, Ammar Abbas, Mateusz Łajszczak +5
Generating expressive and contextually appropriate prosody remains a challenge for modern text-to-speech (TTS) systems. This is particularly evident for long, multi-sentence inputs…
CopyCat2: A Single Model for Multi-Speaker TTS and Many-to-Many Fine-Grained Prosody Transfer
Sri Karlapati, Penny Karanasou, Mateusz Lajszczak +7
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…
Multi-Scale Spectrogram Modelling for Neural Text-to-Speech
Ammar Abbas, Bajibabu Bollepalli, Alexis Moinet +6
We propose a novel Multi-Scale Spectrogram (MSS) modelling approach to synthesise speech with an improved coarse and fine-grained prosody. We present a generic multi-scale spectrog…
Imitation Learning for Neural Morphological String Transduction
Peter Makarov, Simon Clematide
We employ imitation learning to train a neural transition-based string transducer for morphological tasks such as inflection generation and lemmatization. Previous approaches to tr…
Align and Copy: UZH at SIGMORPHON 2017 Shared Task for Morphological Reinflection
Peter Makarov, Tatiana Ruzsics, Simon Clematide
This paper presents the submissions by the University of Zurich to the SIGMORPHON 2017 shared task on morphological reinflection. The task is to predict the inflected form given a…