82 citations · 103 across the 9 of their papers we have counts for
10 papers
Vocal effort modeling in neural TTS for improving the intelligibility of synthetic speech in noise
Tuomo Raitio, Petko Petkov, Jiangchuan Li +3
We present a neural text-to-speech (TTS) method that models natural vocal effort variation to improve the intelligibility of synthetic speech in the presence of noise. The method c…
Combining speakers of multiple languages to improve quality of neural voices
Javier Latorre, Charlotte Bailleul, Tuuli Morrill +2
In this work, we explore multiple architectures and training procedures for developing a multi-speaker and multi-lingual neural TTS system with the goals of a) improving the qualit…
Evaluating the Intelligibility Benefits of Neural Speech Enrichment for Listeners with Normal Hearing and Hearing Impairment using the Greek Harvard Corpus
Muhammed PV Shifas, Anna Sfakianaki, Theognosia Chimona +1
In this work we evaluate a neural based speech intelligibility booster based on spectral shaping and dynamic range compression (SSDRC), referred to as WaveNet-based SSDRC (wSSDRC),…
Enhancing Speech Intelligibility in Text-To-Speech Synthesis using Speaking Style Conversion
Dipjyoti Paul, Muhammed PV Shifas, Yannis Pantazis +1
The increased adoption of digital assistants makes text-to-speech (TTS) synthesis systems an indispensable feature of modern mobile devices. It is hence desirable to build a system…
Speaker Conditional WaveRNN: Towards Universal Neural Vocoder for Unseen Speaker and Recording Conditions
Dipjyoti Paul, Yannis Pantazis, Yannis Stylianou
Recent advancements in deep learning led to human-level performance in single-speaker speech synthesis. However, there are still limitations in terms of speech quality when general…
A non-causal FFTNet architecture for speech enhancement
Muhammed PV Shifas, Nagaraj Adiga, Vassilis Tsiaras +1
In this paper, we suggest a new parallel, non-causal and shallow waveform domain architecture for speech enhancement based on FFTNet, a neural network for generating high quality a…