منابع مشابه
A trainable excitation model for HMM-based speech synthesis
This paper introduces a novel excitation approach for speech synthesizers in which the final waveform is generated through parameters directly obtained from Hidden Markov Models (HMMs). Despite the attractiveness of the HMM-based speech synthesis technique, namely utilization of small corpora and flexibility concerning the achievement of different voice styles, synthesized speech presents a cha...
متن کاملA novel irregular voice model for HMM-based speech synthesis
State-of-the-art text-to-speech (TTS) synthesis is often based on statistical parametric methods. Particular attention is paid to hidden Markov model (HMM) based text-to-speech synthesis. HMM-TTS is optimized for ideal voices and may not produce high quality synthesized speech with voices having frequent non-ideal phonation. Such a voice quality is irregular phonation (also called as glottaliza...
متن کاملHmm-based Incremental Speech Synthesis
Incremental speech synthesis aims at delivering the synthetic voice while the sentence is still being typed. The main challenges are the online estimation of the target prosody from a partial knowledge of the sentence’s syntactic structure, the online phonetization and estimation of parts of speech and the timing of the delivery. This thesis aims at solving these challenges resulting in the imp...
متن کاملAmplitude Spectrum based Excitation Model for HMM-based Speech Synthesis
This paper describes an excitation model based on amplitude spectrum for hidden Markov model (HMM)-based speech synthesis system (HTS). Residual signal obtained from inverse filtering is decomposed into periodic and aperiodic spectrums in frequency domain. Amplitude spectrum of half pitch period length is reserved as periodic component in synthesis stage and zero-phase criterion and pitch synch...
متن کاملA Bengali HMM Based Speech Synthesis System
The paper presents the capability of an HMM-based TTS system to produce Bengali speech. In this synthesis method, trajectories of speech parameters are generated from the trained Hidden Markov Models. A final speech waveform is synthesized from those speech parameters. In our experiments, spectral properties were represented by Mel Cepstrum Coefficients. Both the training and synthesis issues a...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Engineering Journal
سال: 2018
ISSN: 0125-8281
DOI: 10.4186/ej.2018.22.1.187