Isolated uttered words recognition based on GMM/HMM algorithms using SoPC/Nios II processor build on Altera Cyclone II FPGA chip

A flexible accelerator hardware for full-search vector quantization (VQ) has been developed as a component for a system on a programmable chip (SoPC) to use in real-time image compression and recognition applications. Nowadays, FPGA and its SoPC (System on Programmable Chip) tools are powerful enough to efficiently develop a flexible hardware accelerator for VQ application. In addition, one of statistical analysis methods, weighted modular principal component analysis, has showed efficiencies in recognition applications. In this paper, a parallel architecture for online face recognition using weighted modular principal component analysis (WMPCA) and its system-on-programmable-chip (SoPC) implementation are discussed.

This paper describes how formant frequencies of voiced and unvoiced speech can be predicted from mel-frequency cepstral coefficients (MFCC) vectors using maximum a posteriori (MAP) estimation within a hidden Markov model (HMM) framework. Gaussian mixture models (GMMs) are used to model the local joint density of MFCCs and formant frequencies. More localised prediction is achieved by modelling speech using voiced, unvoiced and non-speech GMMs for every state of each model of a set of HMMs. To predict formant frequencies from a MFCC vector, first a prediction of the speech class (voiced, unvoiced or non-speech) is made. Formant frequencies are predicted from voiced and unvoiced speech using a MAP estimation made using the state-specific GMMs. This 'eHMM-GMM' prediction of speech class and formant frequencies was evaluated on a male 5000 word unconstrained large vocabulary speaker-independent database.

Exploring similarity-based classification of larynx disorders from human voice

Condition monitoring techniques are described in this chapter. Two aspects of condition monitoring process are considered: (1) feature extraction; and (2) condition classification. Feature extraction methods described and implemented are fractals, kurtosis, and Mel-frequency cepstral coefficients. Classification methods described and implemented are support vector machines (SVM), hidden Markov models (HMM), Gaussian mixture models (GMM), and extension neural networks (ENN). The effectiveness of these features was tested using SVM, HMM, GMM, and ENN on condition monitoring of bearings and are found to give good results.

A hybrid model comprised of Gaussian Mixtures Models (GMMs) and Hidden Markov Models (HMMs) is used to model generic sounds with large intra class perceptual variations. Each class has variable number of mixture components in the GMM. The number of mixture components is derived using the Minimum Description Length (MDL) criterion. The overall performance of the hybrid model was compared against models based on HMMs and GMMs with a fixed number of mixture components across all classes. We show that a hybrid model outperforms both class-based GMMs, HMMs, and GMMs based on fixed number of components. Further, our experiments revealed that the contribution of transitions between states in HMMs has no significant effect on the overall classification performance of generic sounds when large intra class perceptual variations are present among sounds in the training and test datasets. Sounds that show multi-event structure with events that tend to be similar (repetitive) indicated improved performance when modeled with HMMs that can be attributed to HMM’s state transition property. Conversely, GMMs indicate better performance when the sound samples show subtle or no repetitive behavior. These results were validated using the MuscleFish sound database.

As well-known, system on a programmable chip (SOPC) is widely used in a variety of field control systems , due to their flexible configurations and intelligent stand-alone characteristics. They are also increasingly used in astronomical instrument control nowadays. For those complex and diverse systems, a number of different control strategies are stored in FLASH, but the controller of on-chip determines which one to load. At the same time, it can be switched intelligently and remotely to form a multi-strategy control system, so as to extend the control functions and achieve system on-line reconfiguration quickly. In this paper we describe a design concept and realization method of a multistrategy control system on the basis of FPGA-based system on a chip. Its hardware core is Altera's Cyclone series EP3C25 chip .In SOPC BUILDER development environment ,a control system is constructed, which consists of NIOS II soft core as CPU ,REMOTE_UPDATE IP core and control algorithms as well. The concept and design has been verified in the field controllers for various astronomical applications. Satisfactory results have been obtained.

As one of biometric identification technologies, speaker recognition shows better application prospects in many fields. At present, the implementation of speaker recognition algorithm on the hardware is mostly based on System on a Programmable Chip(SOPC) platform of Field Programmable Gate Array(FPGA) with Nios II Intellectual Property(IP) core. And the algorithm can be selected and optimized effectively on SOPC. However the high-speed and parallel operation of FPGA can't be fully utilized. By researching and analyzing the speaker recognition algorithm, a FPGA-based speaker recognition method is presented in this paper. This method includes the Voice Active Detection (VAD), Mel Frequency Cepstrum Coefficient (MFCC) extraction and Vector Quantization (VQ) recognition algorithm. By using the technology such as Ping-pang operation, Pipeline and module reuse, it makes full use of FPGA's speed. After testing, this method can effectively meet the requirement of real-time data processing.

A stepper motor subdivided control system based on SOPC (system on a programmable chip) technology is designed in this paper. Using the TFG (task flow graph) model, the system is designed to satisfy the requirement of some micromanipulation. The design method is based on the TFG model of the digital signal process algorithm and implemented with SOPC technology. As is known, stepper motor system consists of three parts which are control section, driver section and stepper motor. Under the analysis of the stepper motor subdivided principle which gives its precise control method, the architecture of the system and the practical driver circuit as the control circuit are provided. The Nios II CPU is selected for the SOPC system as the control core. The software flow process chart of the subdivided control according to the timing requirement of LMD18245 which is the driver chip for the stepper motor is also offered. In the end, the picture of the substance of the system is shown.

Comparison of Khasi speech representations with different spectral features and hidden Markov states

Dialect Identification is the process of identifies the dialects of particular standard language. The Telugu Language is one of the historical and important languages. Like any other language Telugu also contains mainly three dialects Telangana, Costa Andhra and Rayalaseema. The research work in dialect identification is very less compare to Language identification because of dearth of database. In any dialects identification system, the database and feature engineering play vital roles because of most the words are similar in pronunciation and also most of the researchers apply statistical approaches like Hidden Markov Model (HMM), Gaussian Mixture Model (GMM), etc. to work on speech processing applications. But in today's world, neural networks play a vital role in all application domains and produce good results. One of the types of the neural networks is Deep Neural Networks (DNN) and it is used to achieve the state of the art performance in several fields such as speech recognition, speaker identification. In this, the Deep Neural Network (DNN) based model Multilayer Perceptron is used to identify the regional dialects of the Telugu Language using enhanced Mel Frequency Cepstral Coefficients (MFCC) features. To do this, created a database of the Telugu dialects with the duration of 5h and 45m collected from different speakers in different environments. The results produced by DNN model compared with HMM and GMM model and it is observed that the DNN model provides good performance.

This contribution investigates the use of features derived from a Gabor filterbank (GFB) for the application of acoustic cough classification. Gabor filters are two-dimensional filters that decompose the spectro-temporal power density further into components which capture spectral, temporal and joint spectro-temporal modulation patterns. The proposed GFB feature extraction scheme in combination with Gaussian mixture model (GMM) and hidden Markov model (HMM) classifier back-ends is evaluated using a cough database recorded by a phone hotline. The database is composed of two kind of coughs, i.e., dry and productive cough, and other sounds, e.g. speech. Based on these data, we show that GFB features result in better recognition performance than the common Mel-frequency cepstral coefficient (MFCC) baseline for the given task of cough classification. Furthermore, results indicate that GMMs are preferable to HMMs for this kind of data.

Objective: Accurate dialect identification technique helps in improving the speech recognition systems that exist in most of the present day electronic devices and is also expected to help in providing new services in the field of e-health and telemedicine which is especially important for older and homebound people. Methods: In this paper we have developed the speech corpora needed for the dialect identification purpose and described a method to identify Assamese language (an Indian language) and two of its dialects, viz., Kamrupi and Goalparia. Findings: Research work done on dialect identification is relatively much less than that on language identification for which one of the reasons being dearth of sufficient database on dialects. As mentioned, we have developed the database and then Mel-Frequency Cepstral Coefficient has been used to extract the spectral features of the collected speech data. Two modeling techniques, namely, Gaussian Mixture Model and Gaussian Mixture Model with Universal Background Model have been used as the modeling techniques to identify the dialects and language. Novelty: So far, standard speech database for Assamese dialects that can be used for speech processing research has not been made. In this paper, we not only describe a method to identify dialects of the Assamese language, but have also developed the speech corpora needed for the dialect identification purpose. Keywords: Assamese, Gaussian Mixture Model, Gaussian Mixture Model with Universal Background Model, Goalparia, Kamrupi, Mel-Frequency Cepstral Coefficient

Multispecies discrimination of New World Warblers (Family Parulidae) using Hidden Markov models (HMMS)

Automatic speaker identification techniques are widely used nowadays in forensic applications, but its accuracy harshly drops when the voice of the speaker of interest is immersed in a recording containing more than one voice, common situation of investigations where the targets voice are obtained through ambient recordings. In forensic applications where microphones are hidden, such interferent sound sources in recordings are common and they degrade severely the performance of speaker identification techniques. In this paper, we propose a method to mitigate this problem by spatially separating the voice of each speaker using a Blind Source Separation technique called Convolutive Independent Component Analysis, and then applying the separated speech signals to a speaker identification system based on Mel Frequency Cepstral Coefficients and Gaussian Mixture Models. For identifying more than one speaker, the proposed system has a better accuracy than the state-of-the-art solutions.

Identifying different audio segments in videos is the first step for many important tasks such as event detection and speech transcription. Approaches using Mel-Frequency Cepstral coefficients (MFCCs) with Gaussian mixture models (GMMs) and hidden Markov models (HMMs) perform reasonably well in stationary conditions but do not scale to a broad range of environmental conditions. This paper focuses on the audio segmentation in broadcast soccer videos into audio classes such as Silence, Speech Only, Speech Over Crowd, Crowd Only and Excited, with an alternative feature set which is simplistic as well as robust to changes in the environment conditions. Support Vector Machines (SVMs), Neural Networks and Random Forest are used for the classification. The accuracy achieved with SVMs, Neural Networks and Random Forest are 83.80%, 86.07%, and 88.35% respectively. The proposed features and Random Forest classifier are found to achieve better accuracy compared to the other classifiers.