scholarly journals Telugu Speech Recognition on LSF and DNN Techniques

2019 ◽  
Vol 8 (4) ◽  
pp. 7160-7162
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Deep Level ◽  
Human Interaction ◽  
Major Area ◽  
Feature Classification ◽  
Human Machine Interaction ◽  
Easy Task ◽  
Recognition Systems ◽  
Machine Interaction

This fast world is running with machine and human interaction. This kind of interaction is not an easy task. For proper interaction between human and machine speech recognition is major area where the machine should understand the speech properly to perform the tasks. So ASR have been developed which improvised the HMIS (“Human Machine Interaction systems”) technology in to the deep level. This research focuses on speech recognition over “Telugu language”, which is used in Telugu HMI systems. This paper uses LSF (linear spectral frequencies) technique for feature extraction and DNN for feature classification which finally produced the effective results. Many other recognition systems also used these techniques but for Telugu language this are the most suitable techniques.

scholarly journals A Conceptual Framework for Machine Self-Presentation and Trust

2021 ◽  
Vol 2 (1) ◽  
pp. 20-45
Author(s):  
Jeff Stanley ◽  
Ozgur Eris ◽  
Monika Lohani
Keyword(s):  
Literature Review ◽  
Research And Development ◽  
Conceptual Framework ◽  
Human Interaction ◽  
Future Research ◽  
Human Machine Interaction ◽  
Self Presentation ◽  
Verbal Behaviors ◽  
Psychosocial Model ◽  
Machine Interaction

Increasingly, researchers are creating machines with humanlike social behaviors to elicit desired human responses such as trust and engagement, but a systematic characterization and categorization of such behaviors and their demonstrated effects is missing. This paper proposes a taxonomy of machine behavior based on what has been experimented with and documented in the literature to date. We argue that self-presentation theory, a psychosocial model of human interaction, provides a principled framework to structure existing knowledge in this domain and guide future research and development. We leverage a foundational human self-presentation taxonomy (Jones and Pittman, 1982), which associates human verbal behaviors with strategies, to guide the literature review of human-machine interaction studies we present in this paper. In our review, we identified 36 studies that have examined human-machine interactions with behaviors corresponding to strategies from the taxonomy. We analyzed frequently and infrequently used strategies to identify patterns and gaps, which led to the adaptation of Jones and Pittman’s human self-presentation taxonomy to a machine self-presentation taxonomy. The adapted taxonomy identifies strategies and behaviors machines can employ when presenting themselves to humans in order to elicit desired human responses and attitudes. Drawing from models of human trust we discuss how to apply the taxonomy to affect perceived machine trustworthiness.

Phase Autocorrelation Bark Wavelet Transform (PACWT) Features for Robust Speech Recognition

2015 ◽  
Vol 40 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Sayf A. Majeed ◽  
Hafizah Husain ◽  
Salina A. Samad
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Speech Recognition ◽  
Extraction Method ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Feature Extraction Method ◽  
Female Data ◽  
Recognition Systems ◽  
New Feature

Abstract In this paper, a new feature-extraction method is proposed to achieve robustness of speech recognition systems. This method combines the benefits of phase autocorrelation (PAC) with bark wavelet transform. PAC uses the angle to measure correlation instead of the traditional autocorrelation measure, whereas the bark wavelet transform is a special type of wavelet transform that is particularly designed for speech signals. The extracted features from this combined method are called phase autocorrelation bark wavelet transform (PACWT) features. The speech recognition performance of the PACWT features is evaluated and compared to the conventional feature extraction method mel frequency cepstrum coefficients (MFCC) using TI-Digits database under different types of noise and noise levels. This database has been divided into male and female data. The result shows that the word recognition rate using the PACWT features for noisy male data (white noise at 0 dB SNR) is 60%, whereas it is 41.35% for the MFCC features under identical conditions

scholarly journals Hybrid methodological approach to context-dependent speech recognition

2017 ◽  
Vol 14 (1) ◽  
pp. 172988141668713 ◽  
Author(s):  
Dragiša Mišković ◽  
Milan Gnjatović ◽  
Perica Štrbac ◽  
Branimir Trenkić ◽  
Nikša Jakovljević ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Contextual Information ◽  
Research Question ◽  
Methodological Approach ◽  
Explanatory Power ◽  
Hybrid Approach ◽  
Conversational Agents ◽  
Human Machine Interaction ◽  
Recognition Systems ◽  
Context Dependent

Although the importance of contextual information in speech recognition has been acknowledged for a long time now, it has remained clearly underutilized even in state-of-the-art speech recognition systems. This article introduces a novel, methodologically hybrid approach to the research question of context-dependent speech recognition in human–machine interaction. To the extent that it is hybrid, the approach integrates aspects of both statistical and representational paradigms. We extend the standard statistical pattern-matching approach with a cognitively inspired and analytically tractable model with explanatory power. This methodological extension allows for accounting for contextual information which is otherwise unavailable in speech recognition systems, and using it to improve post-processing of recognition hypotheses. The article introduces an algorithm for evaluation of recognition hypotheses, illustrates it for concrete interaction domains, and discusses its implementation within two prototype conversational agents.

Speech-Controlled Human Machine Interaction (Sprachgesteuerte Mensch-Maschine-Interaktion)

2004 ◽  
Vol 46 (6) ◽  
Author(s):  
Jörg Helbig ◽  
Bernd Schindler
Keyword(s):  
Speech Recognition ◽  
Control Systems ◽  
Data Transmission ◽  
Audio Signals ◽  
Human Machine Interaction ◽  
Industrial Environment ◽  
Input And Output ◽  
Speech Control ◽  
Machine Interaction

SummaryThis paper deals with speech controlled applications in an industrial environment. Starting from the application areas the requirements resulting from the technical specialities of this field are described. On the basis of example applications and experiences in the practical use, conclusions for the technological realization of speech control systems are derived. The focus is given to the input and output of the audio signals, the data transmission to the speech recognition computer and to the design of dialogues and vocabularies.

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