An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Digital processing of speech signals

The Mel-Frequency Cepstral Coefficients (MFCC) feature extraction method is a leading approach for speech feature extraction and current research aims to identify performance enhancements. One of the recent MFCC implementations is the Delta-Delta MFCC, which improves speaker verification. In this paper, a new MFCC feature extraction method based on distributed Discrete Cosine Transform (DCT-II) is presented. Speaker verification tests are proposed based on three different feature extraction methods including: conventional MFCC, Delta-Delta MFCC and distributed DCT-II based Delta-Delta MFCC with a Gaussian Mixture Model (GMM) classifier.

A novel approach for MFCC feature extraction

Speaker Identification (SI) is the process of identifying the speaker from a given utterance by comparing the voice biometrics of the utterance with those utterance models stored beforehand. SI technologies are taken a new direction due to the advances in artificial intelligence and have been used widely in various domains. Feature extraction is one of the most important aspects of SI, which significantly influences the SI process and performance. This systematic review is conducted to identify, compare, and analyze various feature extraction approaches, methods, and algorithms of SI to provide a reference on feature extraction approaches for SI applications and future studies. The review was conducted according to Kitchenham systematic review methodology and guidelines, and provides an in-depth analysis on proposals and implementations of SI feature extraction methods discussed in the literature between year 2011 and 2106. Three research questions were determined and an initial set of 535 publications were identified to answer the questions. After applying exclusion criteria 160 related publications were shortlisted and reviewed in this paper; these papers were considered to answer the research questions. Results indicate that pure Mel-Frequency Cepstral Coefficients (MFCCs) based feature extraction approaches have been used more than any other approach. Furthermore, other MFCC variations, such as MFCC fusion and cleansing approaches, are proven to be very popular as well. This study identified that the current SI research trend is to develop a robust universal SI framework to address the important problems of SI such as adaptability, complexity, multi-lingual recognition, and noise robustness. The results presented in this research are based on past publications, citations, and number of implementations with citations being most relevant. This paper also presents the general process of SI.

Speaker identification features extraction methods: A systematic review

Speaker recognition system may be viewed as working in four stages, namely, analysis, feature extraction, modeling and testing. This paper gives an overview of the major techniques developed in each of these stages. Such a review helps in understanding the developments that have taken place in each stage and also the available choices of techniques, along with their relative merits and demerits. A comparative study of different techniques is done at the end of each section to justify the choice of techniques available in the state-of-the-art speaker recognition systems. The paper is concluded with a discussion on the possible future direction for the development of techniques in each stage.

Analysis, Feature Extraction, Modeling and Testing Techniques for Speaker Recognition

This work demonstrates the usefulness of multiple frame size and rate (MFSR) analysis for speaker recognition under limited data condition. Present day speaker recognition systems assume the availability of sufficient data for modelling and testing. Owing to this, speech signals are analysed with fixed frame size and rate, which may be termed as single frame size and rate (SFSR) analysis. In the limited data condition available training and testing data is small. If we use SFSR analysis, then it may not provide sufficient feature vectors to train and test the speaker. Further, insufficient feature vectors lead to poor speaker modelling during training and may not yield reliable decision during testing. As part of analysis, we demonstrate the use of multiple frame size (MFS), multiple frame rate (MFR) and MFSR analysis techniques for speaker recognition under limited data condition. These techniques are specifically useful to mitigate the sparseness of limited feature vectors during training and testing. These techniques produce relatively more number of feature vectors. This helps in better modelling and testing under limited data conditions. The experimental results show that use of MFS, MFR and MFSR analysis improves the performance significantly compared to SFSR analysis. The MFSR analysis even outperforms the Gaussian mixture model-universal background model (GMM-UBM) performance, the most widely used modelling technique.

Multiple frame size and rate analysis for speaker recognition under limited data condition

In this paper, a spoken query system is demonstrated which can be used to access the latest agricultural commodity prices and weather information in Kannada language using mobile phone. The spoken query system consists of Automatic Speech Recognition (ASR) models, Interactive Voice Response System (IVRS) call flow, Agricultural Marketing Network (AGMARKNET) and India Meteorological Department (IMD) databases. The ASR models are developed by using the Kaldi speech recognition toolkit. The task specific speech data is collected from the different dialect regions of Karnataka (a state in India speaks Kannada language) to develop ASR models. The web crawler is used to get the commodity price and weather information from AGMARKNET and IMD websites. The postgresql database management system is used to manage the crawled data. The 80 and 20% of validated speech data is used for system training and testing respectively. The accuracy and Word Error Rate (WER) of ASR models are highlighted and end to end spoken query system is developed for Kannada language.

A spoken query system for the agricultural commodity prices and weather information access in Kannada language

This work focuses on the task of speaker recognition under limited data conditions. In case of limited data, the amount of available training and testing data will be few seconds. Under such conditions the conventional classifiers will have very few feature vectors for modelling. This work performs an experimental evaluation of three simple modelling techniques namely, direct template matching (DTM), crisp vector quantization (CVQ) and fuzzy vector quantization (FVQ). Among these FVQ shows significant improved performance compared to DTM and CVQ. For about 3 s of training and testing data the performance for DTM, CVQ and FVQ are 76.67, 73.33, and 86.67, respectively, for a set of first 30 speakers taken from the YOHO database.

Fuzzy Vector Quantization for speaker recognition under limited data conditions

Most of the existing modelling techniques for the speaker recognition task make an implicit assumption of sufficient data for speaker modelling and hence may lead to poor modelling under limited data condition. The present work gives an experimental evaluation of the modelling techniques like Crisp Vector Quantization (CVQ), Fuzzy Vector Quantization (FVQ), Self-Organizing Map (SOM), Learning Vector Quantization (LVQ), and Gaussian Mixture Model (GMM) classifiers. An experimental evaluation of the most widely used Gaussian Mixture Model-Universal Background Model (GMM-UBM) is also made. The experimental knowledge is then used to select a subset of classifiers for obtaining the combined classifiers. It is proposed that the combined LVQ and GMM-UBM classifier provides relatively better performance compared to all the individual as well as combined classifiers.

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H. S. Jayanna

Papers

Analysis, Feature Extraction, Modeling and Testing Techniques for Speaker Recognition

Multiple frame size and rate analysis for speaker recognition under limited data condition

A spoken query system for the agricultural commodity prices and weather information access in Kannada language

Fuzzy Vector Quantization for speaker recognition under limited data conditions

An experimental comparison of modelling techniques for speaker recognition under limited data condition