Feature Extraction and Processing Analysis in Speech Recognition

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.