The SIMPLE4ALL project is creating speech synthesis technology that learns from data with little or no expert supervision and continually improves itself, simply by being used.
Publications
Public deliverables
- D1.6 – to be published after final review
- D2.1: Initial synthesis front end description
- D2.2: Description of the final version of the front-end
- D3.1: Automatic parameterization with WLP-based GIF-techniques
- D3.2: Report describing initial version of deep layered models
- D3.3: Evaluation of the new speech signal model in conjunction with HMM-based acoustic models
- D3.4: Report describing the final version of the deep layered models
- D3.5: Final evaluation report
- D4.1: Learning from user-provided data
- D4.2: Online learning for improving one, or more than one component
- D4.3 – to be published after final review
- D5.1: Acoustic and prosodic analysis of genres and speaking styles
- D5.2: Final report on the implementation and evaluation of genre classification
- D6.1: Project website / remote collaboration tools
- D6.3 – to be published after final review
- D6.4 – to be published after final review
- D6.7 – to be published after final review
Internship reports
- Bajibabu Bollepalli: Effect of MPEG audio compression on HMM-based speech synthesis
- Jiunn Lin Wong: Realisation and simulation of the Mel log spectrum approximation filter
- Dalia Popescu: Unsupervised Text Syllabification with Information from Audio
- Srikanth Ronanki: Syllable based models for prosody modelling in HMM based speech synthesis
- Jose Moreno: Effects of Noise on a Speaker-Adaptive Statistical Speech Synthesis System
- Arturo Romero Blanco : Spanish Emotional Speech Synthesis
- Jaime Lorenzo-Trueba: Towards Cross-lingual Emotion Transplantation
- Ascension Gallardo: A Comparison of Open-Source Segmentation Architectures
