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This paper deals with the problems of automatic segmentation for the purposes of Czech concatenative speech synthesis. Statistical approach to speech segmentation using hidden Markov models (HMMs) is applied in the baseline system. Several improvements of this system are then proposed to get more accurate segmentation results. These enhancements mainly(More)
The present paper deals with the evaluation of large-scale listening tests and with the detection of unaccountable or unreliable answers for each listener. The iterative maximum likelihood estimation scheme is proposed and its abilities are demonstrated and discussed on data collected from a large-scale listening test which was carried out with the aim to(More)
The present paper focuses on the unit selection approach to speech synthesis, discussing drawbacks mainly related to the current handling of target features that basically results in the need of huge corpora. In the paper there are outlined possible solutions based on measuring (dis)similarity among prosodic patterns. In the initial experiment, trying to(More)
The paper describes the optimisation of Viterbi search used in unit selection TTS, since with a large speech corpus necessary to achieve a high level of naturalness, the performance still suffers. To improve the search speed, the combination of sophisticated stopping schemes and pruning thresholds is employed into the baseline search. The optimised search(More)
This paper gives a survey of the current state of ARTIC – the modern Czech concatenative corpus-based text-to-speech system. All stages of the system design are described in the paper, including the acoustic unit inventory building process, text processing and speech production issues. Two versions of the system are presented: the single unit instance(More)
This paper presents recent improvements on ARTIC – the modern Czech corpus-based text-to-speech system. As a statistical approach (using hidden Markov models) was applied to create an acoustic unit inventory, several improvements concerning acoustic unit modelling, clustering and segmentation have been accomplished to increase the intelligibility of the(More)
—This paper describes a development of limited domain expressive speech synthesis for the Czech language. Our current speech synthesis system is based on unit selection methods and produces high quality speech in a neutral speaking style. This work focuses on modifications made in the synthesis algorithm to integrate expressivity into generated speech.(More)
The paper deals with the process of designing a phonetically and prosodically rich speech corpus for unit selection speech synthesis. The attention is given mainly to the recording and verification stage of the process. In order to ensure as high quality and consistency of the recordings as possible, a special recording environment consisting of a recording(More)
Aiming at the improvement of the quality of synthetic speech generated by our native TTS ARTIC, we adopted the unit selection method. Our unit selection module is driven by prosody described solely by high-level symbolic features which are linked to the prosody of synthesized phrases through the phenomena of pro-sodic synonymy and homonymy. It was confirmed(More)
This paper deals with the automatic segmentation for Czech Concatenative speech synthesis. Statistical approach to speech segmen-tation using hidden Markov models (HMMs) is applied in the baseline system [1]. Several experiments that concern various issues in the process of building the segmentation system, such as speech parameterization or HMM(More)