Formant Speech Synthesis Based on Trainable Model

2013 ◽  
Vol 303-306 ◽  
pp. 1334-1337
Author(s):  
Zhi Ping Zhang ◽  
Xi Hong Wu
Keyword(s):  
Speech Synthesis ◽  
Synthesis Method ◽  
Experimental Results ◽  
Trajectory Model ◽  
Formant Synthesis ◽  
Speech Data ◽  
Model Training

The authors proposed a trainable formant synthesis method based on the multi-channel Hidden Trajectory Model (HTM). In the method, the phonetic targets, formant trajectories and spectrum states from the oral, nasal, voiceless and background channels were designed to construct hierarchical hidden layers, and then spectrum were generated as observable features. In model training, the phonemic targets were learned from one-hour training speech data and the boundaries of phonemes were also aligned. The experimental results showed that the speech could be reconstructed with the formant trainable model by a source-filter synthesizer.

Biologically inspired motion synthesis method of two-legged robot with compliant feet

Robotica ◽  
2011 ◽  
Vol 29 (7) ◽  
pp. 1049-1057 ◽  
Author(s):  
Teresa Zielinska ◽  
Andrzej Chmielniak
Keyword(s):  
Central Pattern Generator ◽  
Coupled Oscillators ◽  
Synthesis Method ◽  
Pattern Generator ◽  
Experimental Results ◽  
Human Gait ◽  
Legged Robot ◽  
Biologically Inspired ◽  
Reference Pattern ◽  
Periodic Signals

SUMMARYThis work presents biologically inspired method of gait generation. It uses the reference to the periodic signals generated by biological central pattern generator. The coupled oscillators with correction functions are used to produce leg joint trajectories. The human gait is the reference pattern. The features of generated gait are compared to the human walk. The spring-loaded foot design is presented together with experimental results.

scholarly journals Building a natural sounding Text-To-Speech system for the Nepali language: research and development challenges and solutions

Gipan ◽  
2019 ◽  
Vol 4 ◽  
pp. 106-116
Author(s):  
Roop Shree Ratna Bajracharya ◽  
Santosh Regmi ◽  
Bal Krishna Bal ◽  
Balaram Prasain
Keyword(s):  
Research And Development ◽  
Visually Impaired ◽  
Speech Synthesis ◽  
Selection Process ◽  
Synthesis Method ◽  
Text To Speech ◽  
Synthesis System ◽  
Unit Selection ◽  
Language Research ◽  
Shed Light

Text-to-Speech (TTS) synthesis has come far from its primitive synthetic monotone voices to more natural and intelligible sounding voices. One of the direct applications of a natural sounding TTS systems is the screen reader applications for the visually impaired and the blind community. The Festival Speech Synthesis System uses a concatenative speech synthesis method together with the unit selection process to generate a natural sounding voice. This work primarily gives an account of the efforts put towards developing a Natural sounding TTS system for Nepali using the Festival system. We also shed light on the issues faced and the solutions derived which can be quite overlapping across other similar under-resourced languages in the region.

Using API Call Sequences for IoT Malware Classification Based on Convolutional Neural Networks

2021 ◽  
Vol 31 (04) ◽  
pp. 587-612
Author(s):  
Qianguang Lin ◽  
Ni Li ◽  
Qi Qi ◽  
Jiabin Hu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Comprehensive Evaluation ◽  
Optimal Solution ◽  
Classification Problem ◽  
Experimental Results ◽  
Series Data ◽  
Chi Square ◽  
Malware Classification ◽  
Model Training

Internet of Things (IoT) devices built on different processor architectures have increasingly become targets of adversarial attacks. In this paper, we propose an algorithm for the malware classification problem of the IoT domain to deal with the increasingly severe IoT security threats. Application executions are represented by sequences of consecutive API calls. The time series of data is analyzed and filtered based on the improved information gains. It performs more effectively than chi-square statistics, in reducing the sequence lengths of input data meanwhile keeping the important information, according to the experimental results. We use a multi-layer convolutional neural network to classify various types of malwares, which is suitable for processing time series data. When the convolution window slides down the time sequence, it can obtain higher-level positions by collecting different sequence features, thereby understanding the characteristics of the corresponding sequence position. By comparing the iterative efficiency of different optimization algorithms in the model, we select an algorithm that can approximate the optimal solution to a small number of iterations to speed up the convergence of the model training. The experimental results from real world IoT malware sample show that the classification accuracy of this approach can reach more than 98%. Overall, our method has demonstrated practical suitability for IoT malware classification with high accuracies and low computational overheads by undergoing a comprehensive evaluation.

Speech synthesis method

2002 ◽  
Vol 112 (1) ◽  
pp. 21
Author(s):  
Takehiko Kagoshima ◽  
Masami Akamine
Keyword(s):  
Speech Synthesis ◽  
Synthesis Method

Speech synthesis method

2005 ◽  
Vol 117 (3) ◽  
pp. 994
Author(s):  
Takehiko Kagoshima ◽  
Masami Akamine
Keyword(s):  
Speech Synthesis ◽  
Synthesis Method

Unit-Selection Speech Synthesis Method Using Words as Search Units

2016 ◽  
Vol 7 (2) ◽  
pp. 53-67
Author(s):  
Hiroyuki Segi
Keyword(s):  
Conventional Method ◽  
Speech Synthesis ◽  
Synthesis Method ◽  
Unit Selection ◽  
Human Speech ◽  
Subjective Evaluations ◽  
Synthesized Speech ◽  
Opinion Score ◽  
Speech Database

Unit-selection speech-synthesis systems have been proposed. In most of the unit-selection speech-synthesis systems, search units are rather short such as syllables, phonemes and diphones. However, when applied to large speech databases, shorter units produce more voice-waveform candidates and a larger speech database cannot be used without narrow pruning for practical use. Narrow pruning impairs the quality of the synthesized speech. Here the author examined the possibility of using words as search units. Subjective evaluations indicated that 70% of the speech synthesized by the proposed method sounded more natural than that synthesized by a conventional method. The five-point mean opinion score of the synthesized speech was 3.5, and 21% was judged to sound as natural as human speech. These results demonstrate the effectiveness of unit-selection speech synthesis using words as search units.

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