A Novel Approach for Multilingual Speech Recognition with Back Propagation Artificial Neural Network

“Speech Recognition” of audio signal is important for telecommunication, language identification and speaker verification. Robust Speech Recognition can be applied to automation of houses, offices and telecommunication services. In this paper Speech Recognition & Language Identification have done for Bengali, Chhattisgarhi, English and Hindi speech signals. The Bengali, Chhattisgarhi, English, Hindi speech signals are “Ekhone Tumi Jao”, “Ae Bar Teha Ja”, “Now This Time You Go” and “Ab Is Bar tum Jao” respectively. This method is mainly applied in two phases, in the first phase Speech Recognition and Language identification have done with Back Propagation Artificial neural Network (BPANN) and in the second phase Speech Recognition and Language Identification have done with the combination of the Particle Swarm Optimization (PSO) feature selection technique and BPANN. For the feature extraction Mel Frequency Cepstral Coefficients (MFCC) & Linear Predictive Coding (LPC) is used. MFCC and LPC are the most widely used feature extraction method. BPANN is a feed forward type neural network, it can trace back the error signal for weight modification, error signal generates when the actual output value differs from the target output value. The system accuracy and performance is measured on the basis of “Recognition Rate” and amount of error. Multilingual Speech Recognition and Language Identification with PSO feature selection technique gives the better Recognition Rate as compare to the without PSO feature selection technique

Water filtration by using apple and banana peels as activated carbon

Water filter is an important devices for reducing the contaminants in raw water. Activated from charcoal is used to absorb the contaminants. Fruit peels are some of the suitable alternative carbon to substitute the charcoal. Determining the role of fruit peels which were apple and banana peels powder as activated carbon in water filter is the main goal. Drying and blending the peels till they become powder is the way to allow them to absorb the contaminants. Comparing the results for raw water before and after filtering is the observation. After filtering the raw water, the reading for pH was 6.8 which is in normal pH and turbidity reading recorded was 658 NTU. As for the colour, the water becomes more clear compared to the raw water. This study has found that fruit peels such as banana and apple are an effective substitute to charcoal as natural absorbent

Enhancing Speech Recognition Using Improved Particle Swarm Optimization Based Hidden Markov Model

Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO) is suggested. The suggested methodology contains four stages, namely, (i) denoising, (ii) feature mining (iii), vector quantization, and (iv) IPSO based hidden Markov model (HMM) technique (IP-HMM). At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC), mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy

Modeling Text Independent Speaker Identification with Vector Quantization

Speaker identification is one of the most important technology nowadays. Many fields such as bioinformatics and security are using speaker identification. Also, almost all electronic devices are using this technology too. Based on number of text, speaker identification divided into text dependent and text independent. On many fields, text independent is mostly used because number of text is unlimited. So, text independent is generally more challenging than text dependent. In this research, speaker identification text independent with Indonesian speaker data was modelled with Vector Quantization (VQ). In this research VQ with K-Means initialization was used. K-Means clustering also was used to initialize mean and Hierarchical Agglomerative Clustering was used to identify K value for VQ. The best VQ accuracy was 59.67% when k was 5. According to the result, Indonesian language could be modelled by VQ. This research can be developed using optimization method for VQ parameters such as Genetic Algorithm or Particle Swarm Optimization

Differential Evolution to Optimize Hidden Markov Models Training: Application to Facial Expression Recognition

The base system in this paper uses Hidden Markov Models (HMMs) to model dynamic relationships among facial features in facial behavior interpretation and understanding field. The input of HMMs is a new set of derived features from geometrical distances obtained from detected and automatically tracked facial points. Numerical data representation which is in the form of multi-time series is transformed to a symbolic representation in order to reduce dimensionality, extract the most pertinent information and give a meaningful representation to humans. The main problem of the use of HMMs is that the training is generally trapped in local minima, so we used the Differential Evolution (DE) algorithm to offer more diversity and so limit as much as possible the occurrence of stagnation. For this reason, this paper proposes to enhance HMM learning abilities by the use of DE as an optimization tool, instead of the classical Baum and Welch algorithm. Obtained results are compared against the traditional learning approach and significant improvements have been obtained.</p

Perceiving user's intention-for-interaction: A probabilistic multimodal data fusion scheme

International audienceUnderstanding people's intention, be it action or thought, plays a fundamental role in establishing coherent communication amongst people, especially in non-proactive robotics, where the robot has to understand explicitly when to start an interaction in a natural way. In this work, a novel approach is presented to detect people's intention-for-interaction. The proposed detector fuses multimodal cues, including estimated head pose, shoulder orientation and vocal activity detection, using a probabilistic discrete state Hidden Markov Model. The multimodal detector achieves up to 80% correct detection rates improving purely audio and RGB-D based variants

Real-Time Recognition Non-Intrusive Electrical Appliance Monitoring Algorithm for a Residential Building Energy Management System

The concern of energy price hikes and the impact of climate change because of energy generation and usage forms the basis for residential building energy conservation. Existing energy meters do not provide much information about the energy usage of the individual appliance apart from its power rating. The detection of the appliance energy usage will not only help in energy conservation, but also facilitate the demand response (DR) market participation as well as being one way of building energy conservation. However, energy usage by individual appliance is quite difficult to estimate. This paper proposes a novel approach: an unsupervised disaggregation method, which is a variant of the hidden Markov model (HMM), to detect an appliance and its operation state based on practicable measurable parameters from the household energy meter. Performing experiments in a practical environment validates our proposed method. Our results show that our model can provide appliance detection and power usage information in a non-intrusive manner, which is ideal for enabling power conservation efforts and participation in the demand response market.1176Ysciescopu

A novel approach to data mining using simplified swarm optimization

Data mining has become an increasingly important approach to deal with the rapid growth of data collected and stored in databases. In data mining, data classification and feature selection are considered the two main factors that drive people when making decisions. However, existing traditional data classification and feature selection techniques used in data management are no longer enough for such massive data. This deficiency has prompted the need for a new intelligent data mining technique based on stochastic population-based optimization that could discover useful information from data. In this thesis, a novel Simplified Swarm Optimization (SSO) algorithm is proposed as a rule-based classifier and for feature selection. SSO is a simplified Particle Swarm Optimization (PSO) that has a self-organising ability to emerge in highly distributed control problem space, and is flexible, robust and cost effective to solve complex computing environments. The proposed SSO classifier has been implemented to classify audio data. To the author’s knowledge, this is the first time that SSO and PSO have been applied for audio classification. Furthermore, two local search strategies, named Exchange Local Search (ELS) and Weighted Local Search (WLS), have been proposed to improve SSO performance. SSO-ELS has been implemented to classify the 13 benchmark datasets obtained from the UCI repository database. Meanwhile, SSO-WLS has been implemented in Anomaly-based Network Intrusion Detection System (A-NIDS). In A-NIDS, a novel hybrid SSO-based Rough Set (SSORS) for feature selection has also been proposed. The empirical analysis showed promising results with high classification accuracy rate achieved by all proposed techniques over audio data, UCI data and KDDCup 99 datasets. Therefore, the proposed SSO rule-based classifier with local search strategies has offered a new paradigm shift in solving complex problems in data mining which may not be able to be solved by other benchmark classifiers