Selection of features is one of the important tasks in the application like Speaker Identification (SI) and other pattern recognition problems. When multiple features are extracted from the same frame of speech, it is expected that a feature vector would contain redundant features. Redundant features confuse the speaker model in multidimensional space resulting in degraded performance by the sy...

Parkinson’s disease (PD) is a neurodegenerative disorder of the nervous central system and it affects the limbs motor control and the communication skills of the patients. The evolution of the disease can get to the point of affecting the intelligibility of the patient’s speech. The treatments of the PD are mainly focused on improving limb symptoms and their impact on speech production is still...

In this paper, the audio emotion recognition system is proposed that uses a mixture of rule-based and machine learning techniques to improve the recognition efficacy in the audio paths. The audio path is designed using a combination of input prosodic features (pitch, log-energy, zero crossing rates and Teager energy operator) and spectral features (Mel-scale frequency cepstral coefficients). Me...

Speech synthesis and voice conversion techniques can pose threats to current speaker verification (SV) systems. For this purpose, it is essential to develop front end systems that are able to distinguish human speech vs. spoofed speech (synthesized or voice converted). In this paper, for the ASVspoof 2015 challenge, we propose a detector based on combination of cochlear filter cepstral coeffici...

Speech emotion recognition is one of the latest challenges in speech processing and Human Computer Interaction (HCI) in order to address the operational needs in real world applications. Besides human facial expressions, speech has proven to be one of the most promising modalities for automatic human emotion recognition. Speech is a spontaneous medium of perceiving emotions which provides in-de...

Principal Component analysis (PCA) is useful in identifying patterns in data, and expressing data in a manner which highlights their similarities and differences. This concept was extracted to reduce high dimensional Mel‟s Frequency Cepstral Coefficients (MFCC) into low dimensional feature vectors. Since MFCC‟s are high in dimensions and truncation of these dependent coefficients may lead to er...

The Mel Frequency cepstral coefficients are the most widely used feature in speech recognition but they are very sensitive to noise. In this paper to achieve a satisfactorily performance in Automatic Speech Recognition (ASR) applications we introduce a noise robust new set of MFCC vector estimated through following steps. First, spectral mean normalization is a pre-processing which applies to t...

The mobile robot is a system that can move according to function and task. An example an industrial taking objects using remote control system. Robots controlled manual are generally carried out on robots. Many researchers have developed methods, such as image or sound-based control. In this study, the was applied in unobstructed room voice commands. methods used Mel-Frequency Cepstral Coeffici...

Automatic detection of neurological disordered subjects voice mostly relies on parameters extracted from time-domain processing. The calculation of these parameters often requires prior pitch period estimation; which in turn depends heavily on the robustness of pitch detection algorithm. In the present work cepstraldomain processing technique which does not require pitch estimation has been ado...

In the context of automatic speech recognition, the popular Mel Frequency Cepstral Coefficients(MFCC) as features, though perform very well under clean and matched environments, are observed to fail in mismatched conditions.The spectral maxima are often observed to preserve their locations and energies under noisy environments, but are not presented explicitly by the MFCC features. This paper p...