A Speaker-Adaptive HMM-based Vietnamese Text-to-Speech System

Ninh, Duy Khanh

doi:10.1109/kse.2019.8919326

Cited by 4 publications

(2 citation statements)

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“…4). Each frame is autocorrelated and the highest autocorrelation value is chosen for linear predictive analysis (Lakomkin, 2018;Ninh, 2019). In linear predictive analysis, the coefficients are calculated, which are given by (Charles, Vishwas, Ruixi, 2020;Bongomin, et al 2020):…”

Section: Linear Predictive Codingmentioning

confidence: 99%

HMM-based phoneme speech recognition system for the control and command of industrial robots

Naik

2021

Technical Transactions

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In recent years, the integration of human-robot interaction with speech recognition has gained a lot of pace in the manufacturing industries. Conventional methods to control the robots include semi-autonomous, fully-autonomous, and wired methods. Operating through a teaching pendant or a joystick is easy to implement but is not effective when the robot is deployed to perform complex repetitive tasks. Speech and touch are natural ways of communicating for humans and speech recognition, being the best option, is a heavily researched technology. In this study, we aim at developing a stable and robust speech recognition system to allow humans to communicate with machines (roboticarm) in a seamless manner. This paper investigates the potential of the linear predictive coding technique to develop a stable and robust HMM-based phoneme speech recognition system for applications in robotics. Our system is divided into three segments: a microphone array, a voice module, and a robotic arm with three degrees of freedom (DOF). To validate our approach, we performed experiments with simple and complex sentences for various robotic activities such as manipulating a cube and pick and place tasks. Moreover, we also analyzed the test results to rectify problems including accuracy and recognition score.

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Section: Linear Predictive Codingmentioning

confidence: 99%

HMM-based phoneme speech recognition system for the control and command of industrial robots

Naik

2021

Technical Transactions

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“…For under-resourced languages or the first development of artificial speech, HMM-based speech synthesis is a technique commonly applied in many cases [ 5 , 6 , 7 , 8 ]. Despite the advantages of this technique for speech synthesis, some shortcomings concerning naturalness and overall quality have been mentioned in the many implementations in languages around the world, often referred to as buzzy and muffled sound [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Discriminative Multi-Stream Postfilters Based on Deep Learning for Enhancing Statistical Parametric Speech Synthesis

Coto-Jiménez

2021

Biomimetics

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Statistical parametric speech synthesis based on Hidden Markov Models has been an important technique for the production of artificial voices, due to its ability to produce results with high intelligibility and sophisticated features such as voice conversion and accent modification with a small footprint, particularly for low-resource languages where deep learning-based techniques remain unexplored. Despite the progress, the quality of the results, mainly based on Hidden Markov Models (HMM) does not reach those of the predominant approaches, based on unit selection of speech segments of deep learning. One of the proposals to improve the quality of HMM-based speech has been incorporating postfiltering stages, which pretend to increase the quality while preserving the advantages of the process. In this paper, we present a new approach to postfiltering synthesized voices with the application of discriminative postfilters, with several long short-term memory (LSTM) deep neural networks. Our motivation stems from modeling specific mapping from synthesized to natural speech on those segments corresponding to voiced or unvoiced sounds, due to the different qualities of those sounds and how HMM-based voices can present distinct degradation on each one. The paper analyses the discriminative postfilters obtained using five voices, evaluated using three objective measures, Mel cepstral distance and subjective tests. The results indicate the advantages of the discriminative postilters in comparison with the HTS voice and the non-discriminative postfilters.

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