Marathi Speech Synthesis: A Review

This paper seeks to reveal the various aspects of Marathi Speech synthesis. This paper has reviewed research development in the International languages as well as Indian languages and then centering on the development in Marathi languages with regard to other Indian languages. It is anticipated that this work will serve to explore more in Marathi language. DOI: 10.17762/ijritcc2321-8169.15064

Affective Medicine: a review of Affective Computing efforts in Medical Informatics

Background: Affective computing (AC) is concerned with emotional interactions performed with and through computers. It is defined as “computing that relates to, arises from, or deliberately influences emotions”. AC enables investigation and understanding of the relation between human emotions and health as well as application of assistive and useful technologies in the medical domain. Objectives: 1) To review the general state of the art in AC and its applications in medicine, and 2) to establish synergies between the research communities of AC and medical informatics. Methods: Aspects related to the human affective state as a determinant of the human health are discussed, coupled with an illustration of significant AC research and related literature output. Moreover, affective communication channels are described and their range of application fields is explored through illustrative examples. Results: The presented conferences, European research projects and research publications illustrate the recent increase of interest in the AC area by the medical community. Tele-home healthcare, AmI, ubiquitous monitoring, e-learning and virtual communities with emotionally expressive characters for elderly or impaired people are few areas where the potential of AC has been realized and applications have emerged. Conclusions: A number of gaps can potentially be overcome through the synergy of AC and medical informatics. The application of AC technologies parallels the advancement of the existing state of the art and the introduction of new methods. The amount of work and projects reviewed in this paper witness an ambitious and optimistic synergetic future of the affective medicine field

Personalising synthetic voices for individuals with severe speech impairment.

Speech technology can help individuals with speech disorders to interact more easily. Many individuals with severe speech impairment, due to conditions such as Parkinson's disease or motor neurone disease, use voice output communication aids (VOCAs), which have synthesised or pre-recorded voice output. This voice output effectively becomes the voice of the individual and should therefore represent the user accurately. Currently available personalisation of speech synthesis techniques require a large amount of data input, which is difficult to produce for individuals with severe speech impairment. These techniques also do not provide a solution for those individuals whose voices have begun to show the effects of dysarthria. The thesis shows that Hidden Markov Model (HMM)-based speech synthesis is a promising approach for 'voice banking' for individuals before their condition causes deterioration of the speech and once deterioration has begun. Data input requirements for building personalised voices with this technique using human listener judgement evaluation is investigated. It shows that 100 sentences is the minimum required to build a significantly different voice from an average voice model and show some resemblance to the target speaker. This amount depends on the speaker and the average model used. A neural network analysis trained on extracted acoustic features revealed that spectral features had the most influence for predicting human listener judgements of similarity of synthesised speech to a target speaker. Accuracy of prediction significantly improves if other acoustic features are introduced and combined non-linearly. These results were used to inform the reconstruction of personalised synthetic voices for speakers whose voices had begun to show the effects of their conditions. Using HMM-based synthesis, personalised synthetic voices were built using dysarthric speech showing similarity to target speakers without recreating the impairment in the synthesised speech output

SYNTHESIZING DYSARTHRIC SPEECH USING MULTI-SPEAKER TTS FOR DSYARTHRIC SPEECH RECOGNITION

Dysarthria is a motor speech disorder often characterized by reduced speech intelligibility through slow, uncoordinated control of speech production muscles. Automatic Speech recognition (ASR) systems may help dysarthric talkers communicate more effectively. However, robust dysarthria-specific ASR requires a significant amount of training speech is required, which is not readily available for dysarthric talkers. In this dissertation, we investigate dysarthric speech augmentation and synthesis methods. To better understand differences in prosodic and acoustic characteristics of dysarthric spontaneous speech at varying severity levels, a comparative study between typical and dysarthric speech was conducted. These characteristics are important components for dysarthric speech modeling, synthesis, and augmentation. For augmentation, prosodic transformation and time-feature masking have been proposed. For dysarthric speech synthesis, this dissertation has introduced a modified neural multi-talker TTS by adding a dysarthria severity level coefficient and a pause insertion model to synthesize dysarthric speech for varying severity levels. In addition, we have extended this work by using a label propagation technique to create more meaningful control variables such as a continuous Respiration, Laryngeal and Tongue (RLT) parameter, even for datasets that only provide discrete dysarthria severity level information. This approach increases the controllability of the system, so we are able to generate more dysarthric speech with a broader range. To evaluate their effectiveness for synthesis of training data, dysarthria-specific speech recognition was used. Results show that a DNN-HMM model trained on additional synthetic dysarthric speech achieves WER improvement of 12.2% compared to the baseline, and that the addition of the severity level and pause insertion controls decrease WER by 6.5%, showing the effectiveness of adding these parameters. Overall results on the TORGO database demonstrate that using dysarthric synthetic speech to increase the amount of dysarthric-patterned speech for training has a significant impact on the dysarthric ASR systems

Multimodal Adapted Robot Behavior Synthesis within a Narrative Human-Robot Interaction

International audienceIn human-human interaction, three modalities of communication (i.e., verbal, nonverbal, and paraverbal) are naturally coordinated so as to enhance the meaning of the conveyed message. In this paper, we try to create a similar coordination between these modalities of communication in order to make the robot behave as naturally as possible. The proposed system uses a group of videos in order to elicit specific target emotions in a human user, upon which interactive narratives will start (i.e., interactive discussions between the participant and the robot around each video's content). During each interaction experiment, the humanoid expressive ALICE robot engages and generates an adapted multimodal behavior to the emotional content of the projected video using speech, head-arm metaphoric gestures, and/or facial expressions. The interactive speech of the robot is synthesized using Mary-TTS (text to speech toolkit), which is used-in parallel-to generate adapted head-arm gestures [1]. This synthesized multimodal robot behavior is evaluated by the interacting human at the end of each emotion-eliciting experiment. The obtained results validate the positive effect of the generated robot behavior multimodality on interaction

Singing voice resynthesis using concatenative-based techniques

Tese de Doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201

A silent speech system based on permanent magnet articulography and direct synthesis

In this paper we present a silent speech interface (SSI) system aimed at restoring speech communication for individuals who have lost their voice due to laryngectomy or diseases affecting the vocal folds. In the proposed system, articulatory data captured from the lips and tongue using permanent magnet articulography (PMA) are converted into audible speech using a speaker-dependent transformation learned from simultaneous recordings of PMA and audio signals acquired before laryngectomy. The transformation is represented using a mixture of factor analysers, which is a generative model that allows us to efficiently model non-linear behaviour and perform dimensionality reduction at the same time. The learned transformation is then deployed during normal usage of the SSI to restore the acoustic speech signal associated with the captured PMA data. The proposed system is evaluated using objective quality measures and listening tests on two databases containing PMA and audio recordings for normal speakers. Results show that it is possible to reconstruct speech from articulator movements captured by an unobtrusive technique without an intermediate recognition step. The SSI is capable of producing speech of sufficient intelligibility and naturalness that the speaker is clearly identifiable, but problems remain in scaling up the process to function consistently for phonetically rich vocabularies

Challenges in analysis and processing of spontaneous speech

Selected and peer-reviewed papers of the workshop entitled Challenges in Analysis and Processing of Spontaneous Speech (Budapest, 2017