Xavi Gonzalvo

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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)
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 presents advances in Google’s hidden Markov model (HMM)-driven unit selection speech synthesis system. We describe several improvements to the run-time system; these include minimal latency, high-quality and fast refresh cycle for new voices. Traditionally unit selection synthesizers are limited in terms of the amount of data they can handle and(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 estimator, analysing its performance objectively and subjectively.(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)
This paper presents an HMM-driven hybrid speech synthesis approach in which unit selection concatenative synthesis is used to improve the quality of the statistical system using a Local Minimum Generation Error (LMGE) during the synthesis stage. The idea behind this approach is to combine the robustness due to HMMs with the naturalness of concatenated(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)
Pitch movement is a large component of speech prosody, and despite being directly modelled in statistical parametric speech synthesis systems very flat intonation contours are still produced. We present an open-source fully data-driven approach to pitch contour stylisation suitable for speech synthesis based on the SLAM approach. Modifications are proposed(More)