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Hidden Markov Models based text-to-speech (HMM-TTS) synthesis is one of the techniques for generating speech from trained statistical models where spectrum and prosody of basic speech units are modelled altogether. This paper presents the advances in our Spanish HMM-TTS and a perceptual test is conducted to compare it with an extended PSOLA-based(More)
Hidden Markov Models based text-to-speech (HMM-TTS) synthesis is a technique for generating speech from trained statistical models where spectrum, pitch and durations of basic speech units are modelled altogether. The aim of this work is to describe a Spanish HMM-TTS system using CBR as a F0 esti-mator, analysing its performance objectively and(More)
This paper proposes the use of Quantized Hidden Markov Models (QHMMs) for reducing the footprint of conventional parametric HMM-based TTS system. Previously, this technique was successfully applied to automatic speech recognition in embedded devices without loss of recognition performance. In this paper we investigate the construction of different quantized(More)
This paper is a contribution to the recent advancements in the development of high-quality next generation text-to-speech (TTS) synthesis systems. Two of the hottest research topics in this area are oriented towards the improvement of speech expressiveness and flexibility of synthesis. In this context, this paper presents a new TTS strategy called(More)
A restricted domain text-to-speech system oriented to a weather forecast application is presented. This TTS system is embedded in a multimedia interactive service accessible from different media, such as TV, Internet and mobile devices. The requirements of this application give rise to several particularities in the design and implementation of the TTS(More)
This paper describes a multi-domain text-to-speech (MD-TTS) synthesis strategy for generating speech among different domains and so increasing the flexibility of high quality TTS systems. To that effect, the MD-TTS introduces a flexible TTS architecture that includes an automatic domain classification module, which allows MD-TTS systems to be implemented by(More)