LSTM Deep Neural Networks Postfiltering for Improving the Quality of Synthetic Voices

Recent developments in speech synthesis have produced systems capable of outcome intelligible speech, but now researchers strive to create models that more accurately mimic human voices. One such development is the incorporation of multiple linguistic styles in various languages and accents. HMM-based Speech Synthesis is of great interest to many researchers, due to its ability to produce sophisticated features with small footprint. Despite such progress, its quality has not yet reached the level of the predominant unit-selection approaches that choose and concatenate recordings of real speech. Recent efforts have been made in the direction of improving these systems. In this paper we present the application of Long-Short Term Memory Deep Neural Networks as a Postfiltering step of HMM-based speech synthesis, in order to obtain closer spectral characteristics to those of natural speech. The results show how HMM-voices could be improved using this approach.Comment: 5 pages, 5 figure

Forward Attention in Sequence-to-sequence Acoustic Modelling for Speech Synthesis

This paper proposes a forward attention method for the sequenceto- sequence acoustic modeling of speech synthesis. This method is motivated by the nature of the monotonic alignment from phone sequences to acoustic sequences. Only the alignment paths that satisfy the monotonic condition are taken into consideration at each decoder timestep. The modified attention probabilities at each timestep are computed recursively using a forward algorithm. A transition agent for forward attention is further proposed, which helps the attention mechanism to make decisions whether to move forward or stay at each decoder timestep. Experimental results show that the proposed forward attention method achieves faster convergence speed and higher stability than the baseline attention method. Besides, the method of forward attention with transition agent can also help improve the naturalness of synthetic speech and control the speed of synthetic speech effectively.Comment: 5 pages, 3 figures, 2 tables. Published in IEEE International Conference on Acoustics, Speech and Signal Processing 2018 (ICASSP2018

A Multi Model HMM Based Speech Synthesis

The Multi-Space Probability Distribution Hidden Markov model (MSD-HMM) is a discrete model that learns a fundamental frequency feature, however it has been proven that synthesized speeches from that model contain buzziness and hoarseness which affect to an intelligibility of synthesized speeches. This research aims to improve an intelligibility of synthesized speeches by proposing a multi model HMM based speech synthesis which it models spectral features and fundamental frequency features separately called spectral model and fundamental frequency model instead of combining them to a same model. The fundamental frequency model is modelled by MSD-HMM. Output durations are calculated from maximum probability of both models. A voicing condition restriction rule with minimum output duration criteria are proposed to prevent an unmatched voicing condition of the generated parameter. Objective results show that the proposed multi model is comparable to the shared model while subjective results show that the proposed model with voicing condition restriction rule and without voicing condition restriction rule is outperform the shared model and reduce the buzziness and hoarseness of the synthesized voice. Intelligibility MOS scores of the proposed model with a voicing condition restriction, the proposed model without a voicing condition restriction and the share model are 3.62, 3.69 and 3.08 respectively and naturalness MOS scores are 3.71, 3.71 and 3.14 respectively.The Multi-Space Probability Distribution Hidden Markov model (MSD-HMM) is a discrete model that learns a fundamental frequency feature, however it has been proven that synthesized speeches from that model contain buzziness and hoarseness which affect to an intelligibility of synthesized speeches. This research aims to improve an intelligibility of synthesized speeches by proposing a multi model HMM based speech synthesis which it models spectral features and fundamental frequency features separately called spectral model and fundamental frequency model instead of combining them to a same model. The fundamental frequency model is modelled by MSD-HMM. Output durations are calculated from maximum probability of both models. A voicing condition restriction rule with minimum output duration criteria are proposed to prevent an unmatched voicing condition of the generated parameter. Objective results show that the proposed multi model is comparable to the shared model while subjective results show that the proposed model with voicing condition restriction rule and without voicing condition restriction rule is outperform the shared model and reduce the buzziness and hoarseness of the synthesized voice. Intelligibility MOS scores of the proposed model with a voicing condition restriction, the proposed model without a voicing condition restriction and the share model are 3.62, 3.69 and 3.08 respectively and naturalness MOS scores are 3.71, 3.71 and 3.14 respectively

Towards Personalized Synthesized Voices for Individuals with Vocal Disabilities: Voice Banking and Reconstruction

When individuals lose the ability to produce their own speech, due to degenerative diseases such as motor neurone disease (MND) or Parkinson’s, they lose not only a functional means of communication but also a display of their individual and group identity. In order to build personalized synthetic voices, attempts have been made to capture the voice before it is lost, using a process known as voice banking. But, for some patients, the speech deterioration frequently coincides or quickly follows diagnosis. Using HMM-based speech synthesis, it is now possible to build personalized synthetic voices with minimal data recordings and even disordered speech. The power of this approach is that it is possible to use the patient’s recordings to adapt existing voice models pre-trained on many speakers. When the speech has begun to deteriorate, the adapted voice model can be further modified in order to compensate for the disordered characteristics found in the patient’s speech. The University of Edinburgh has initiated a project for voice banking and reconstruction based on this speech synthesis technology. At the current stage of the project, more than fifteen patients with MND have already been recorded and five of them have been delivered a reconstructed voice. In this paper, we present an overview of the project as well as subjective assessments of the reconstructed voices and feedback from patients and their families

How to improve TTS systems for emotional expressivity

Several experiments have been carried out that revealed weaknesses of the current Text-To-Speech (TTS) systems in their emotional expressivity. Although some TTS systems allow XML-based representations of prosodic and/or phonetic variables, few publications considered, as a pre-processing stage, the use of intelligent text processing to detect affective information that can be used to tailor the parameters needed for emotional expressivity. This paper describes a technique for an automatic prosodic parameterization based on affective clues. This technique recognizes the affective information conveyed in a text and, accordingly to its emotional connotation, assigns appropriate pitch accents and other prosodic parameters by XML-tagging. This pre-processing assists the TTS system to generate synthesized speech that contains emotional clues. The experimental results are encouraging and suggest the possibility of suitable emotional expressivity in speech synthesis