Speech Synthesis Based on Hidden Markov Models and Deep Learning
| Autor: | Marvin Coto-Jiménez, John Goddard-Close |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Statistical parametric speech synthesis
Computer science Speech recognition Speech synthesis 02 engineering and technology computer.software_genre Machine learning Markov model Viterbi algorithm 030507 speech-language pathology & audiology 03 medical and health sciences symbols.namesake 0202 electrical engineering electronic engineering information engineering Hidden Markov model business.industry Deep learning Maximum-entropy Markov model 020206 networking & telecommunications General Medicine Variable-order Bayesian network symbols Long short-term memory (LSTM) Hidden Markov Models (HMM) Artificial intelligence 0305 other medical science business computer |
| Zdroj: | Research in Computing Science, vol.112, pp.19-28. Kérwá Universidad de Costa Rica instacron:UCR |
| ISSN: | 1870-4069 |
| DOI: | 10.13053/rcs-112-1-2 |
| Popis: | Speech synthesis based on Hidden Markov Models (HMM) and other statistical parametric techniques have been a hot topic for some time. Using this techniques, speech synthesizers are able to produce intelligible and flexible voices. Despite progress, the quality of the voices produced using statistical parametric synthesis has not yet reached the level of the current predominant unit-selection approaches, that select and concatenate recordings of real speech. Researchers now strive to create models that more accurately mimic human voices. In this paper, we present our proposal to incorporate recent deep learning algorithms, specially the use of Long Short-term Memory (LSTM) to improve the quality of HMM-based speech synthesis. Thus far, the results indicate that HMM-voices can be improved using this approach in its spectral characteristics, but additional research should be conducted to improve other parameters of the voice signal, such as energy and fundamental frequency, to obtain more natural sounding voices. Universidad de Costa Rica/[]/UCR/Costa Rica Consejo Nacional de Ciencia y Tecnología/[CB-2012-01, No.182432]/CONACyT/México UCR::Vicerrectoría de Docencia::Ingeniería::Facultad de Ingeniería::Escuela de Ingeniería Eléctrica |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|