Feature Extraction and Processing Analysis in Speech Recognition

Mrinal Paliwal; Pankaj Saraswat

doi:10.55524/ijircst.2021.9.6.12

Abstract

The difficulties with automated identification and synthesis of various speech patterns have become significant research issues in recent years. Stress-induced speech characteristics were compared to normal speech in a feature analysis. Due to stress, the performance of Stressed speech recognition decreases substantially. In the speech communication system, the voice signal is transmitted, stored, and processed in a variety of ways. The speech signal must be delivered in such a way that the information content may be easily extracted from human listeners or machine automation. To enhance speech recognition performance, a stressed compensation method is employed to compensate for stress distortion. To identify different moods in speech signals, these features are collected and assessed in English. The variations in glottal excitement of common speaking patterns are examined in depth in this article. The sinusoidal model effectively describes the different stress classes in a speech signal, according to the results. When it comes to detecting emotions in a pressured speaker, sinusoidal features outperform linear prediction features.

Keywords

Feature Extraction, Processing, Sinusoidal Model, Speech recognition, Stressed.

References

Rahurkar MA, Hansen JHL, Meyerhoff J, Saviolakis G, Koenig M. Frequency band analysis for stress detection using a teager energy operator based feature. In: 7th International Conference on Spoken Language Processing, ICSLP 2002. 2002.
Wang Y. Speech recognition under stress. ProQuest Dissertations and Theses. 2009.
Keller E. The analysis of voice quality in speech processing. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005.
Panda SP, Nayak AK. An efficient model for text-to-speech synthesis in Indian languages. Int J Speech Technol. 2015;
Waghmare K, Kayte S, Gawali B. Analysis of Pitch and Duration in Speech Synthesis using PSOLA. Commun Appl Electron. 2016;
Panda SP, Nayak AK. Automatic speech segmentation in syllable centric speech recognition system. Int J Speech Technol. 2016;
Ghahramani Z. An introduction to hidden Markov models and Bayesian networks. Int J Pattern Recognit Artif Intell. 2001;
Mohanty MN, Jena B. Analysis of stressed human speech. Int J Comput Vis Robot. 2011;
Palo HK, Mohanty MN, Chandra M. Design of neural network model for emotional speech recognition. In: Advances in Intelligent Systems and Computing. 2015.
Palo HK, Mohanty MN, Chandra M. Efficient feature combination techniques for emotional speech classification. Int J Speech Technol. 2016;

Cites this article as

M. Paliwal, P. Saraswat, "Feature Extraction and Processing Analysis in Speech Recognition", International Journal of Innovative Research in Engineering & Management (ijircst), Vol-9, Issue-6, Page No-52-55, 2021. Available from: https://doi.org/10.55524/ijircst.2021.9.6.12

Corresponding Author

Mrinal Paliwal

SOEIT, Sanskriti University, Mathura, Uttar Pradesh, India (mrinalpaliwal.cse@sanskriti.edu.in)

Download Full Paper

Download PDF

No. of Downloads: 32 | No. of Views: 1060

Real-Time Detection of Data Exfiltration Using Deep Learning in Edge Computing Systems

Shivaraj Yanamandram Kuppuraju, Sharad Shyam Ojha, Mrinal Kumar.

March 2025 - Vol 13, Issue 2
AI-Based Encryption Techniques for Securing Data Transmission in Telecommunication Systems

Shiva Kiran Lingishetty, Chandrashekhar Moharir, Mrinal Kumar.

March 2025 - Vol 13, Issue 2
Leveraging Reinforcement Learning for Efficient Task Scheduling in Multi-Cloud Environments

Prasanna Sankaran, Shiva Kiran Lingishetty, Mrinal Kumar.

March 2025 - Vol 13, Issue 2

Feature Extraction and Processing Analysis in Speech Recognition

Citations

Download Full Paper PDF

Total View 1060

Total Download 32