Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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

This paper motivates the use of combination of mel frequency cepstral coefficients (MFCC) and its delta derivatives (DMFCC and DDMFCC) calculated using mel spaced Gaussian filter banks for text independent speaker recognition. MFCC modeled on the human auditory system shows robustness against noise and session changes and hence has become synonymous with speaker recognition. Our main aim is to test the accuracy of our proposed feature set for different values of frame overlap and MFCC feature vector sizes to identify the system having highest accuracy. Principal component analysis (PCA) is applied before the training and testing stages for feature dimensionality reduction thereby increasing computing speed and puts low constraint on the memory required for processing. The use of probabilistic neural network (PNN) in the modeling domain provided the advantages of achieving lower operational times during the training stages. The experiments examined the percentage identification accuracy (PIA) of MFCC, combination of MFCC and DMFCC as well as combination of all three feature sets MFCC, DMFCC and DDMFCC. The proposed feature set attains an identification accuracy of 94% for frame overlap of 90% and MFCC feature size of 18 coefficients. It outperforms the identification rates of the other two feature sets. These speaker recognition experiments were tested using the Voxforge database.

A unique approach in text independent speaker recognition using MFCC feature sets and probabilistic neural network

A systematic literature review of methods and datasets for anomaly-based network intrusion detection

Speech processing is now an emerging technology of signal processing. Some research areas of speech processing are recognition of speech, speaker identification (SI), speech synthesis etc. Speaker identification is important research area of speech processing. SI means identifying the speaker based on his spoken speech. The main use of SI is to recognize the speech owner based on the speaking style of the speaker. SI is mainly used in forensic analysis, home control system, database access services etc. For SI two things are essential. One is feature extraction and another is feature matching. Feature extraction is extraction of small information from the available audio wave signal. That information can be used to represent the particular speaker. For SI, There are many feature extraction techniques like LPC (Linear Predictive Coefficients), MFCC (Mel Frequency Cepstral Coefficients), PLP (Perceptual Linear Predictive Coefficients) and many more are used. MFCC is one of them and it gives good (efficient) identification results. Factor affecting on SI is noise, sampling rate, number of frames etc., and among them noise is the most critical factor. We found that MFCC is not much effective in the noisy environment, especially when the noise condition mismatch. The identification rate becomes poor and poor when the noise level increases. To improve the performance of SI in a real world noisy environment, we propose a technique which is a variant of MFCC. Proposed MFCC includes wiener filter which is good for handling the noise in speech. In this paper, it is suggested that the wiener filter is effective in the frequency domain rather than the time domain based on our experiments. We got 88.57% average identification rate with NOIZEUS database by our proposed technique. In feature matching, the unknown speech is classified by using some classifier. We have used neural network for feature matching.

Mel Frequency Cepstral Coefficients (MFCC) based speaker identification in noisy environment using wiener filter

Optimization of data-driven filterbank for automatic speaker verification

Over the decade, mel-frequency cepstral coefficient (MFCC) has been the most popular feature extraction method in the field of automatic speaker recognition. But in case of robust speaker recognition system, its performance is good for white noise contamination but not as good for other noises. We introduce speech-signal-based frequency cepstral coefficients (SFCC) in speaker recognition domain. In this method, frequency warping function is derived directly from the speech signal itself by considering equal area portions of the logarithm of the ensemble average short-time power spectrum of entire speech corpus. Speech-signal-based frequency warping function is very much similar to the frequency scale obtained through psycho-acoustic experiments known as mel scale and bark scale. We have proposed to use combination of filter banks of both the MFCC and SFCC in text-independent speaker identification. Speaker identification experiments are performed on POLY-COST database. The proposed technique gives better performance than the single streamed MFCC or SFCC based features for robust speaker identification system.

A novel approach in feature level for robust text-independent speaker identification system

Development of automatic speaker verification system (ASV) for real-world applications remains a major challenge. In this paper, we propose an improved speech-signal-based frequency warping scale to extract cepstral features from the speech signal for ASV application. The proposed scale is a modified version of the speech-signal-based scale, successfully used in speech recognition application, an allied domain. It uses spectral entropy weighted power spectral density to extract speaker specific attributes. This is complementary to fixed scale based mel frequency cepstral coefficient (MFCC) for different emphasis given to spectral regions. The work uses fusion based approach to exploit the complementarity of static MFCC and proposed feature. The performances of the ASV system that uses MFCC and the proposed technique are evaluated in clean and various noisy conditions on publicly available NIST SRE databases. Noise database (NOISEX-92) is used to simulate the noisy environment. The ASV system developed from the proposed feature extraction method shows slightly improved performance than baseline MFCC and SFCC (speech-signal-based frequency cepstral coefficient) based techniques in clean condition and up to 38.15% and 17.15%, respectively in noisy conditions. The fusion-based approach further improves the performance of ASV system with up to 53.85% and 36.22% relative improvement over baseline MFCC and SFCC based feature extraction methods, respectively.

Improved Speech-Signal Based Frequency Warping Scale for Cepstral Feature in Robust Speaker Verification System

In different speech-based applications, the speechsignal-based frequency cepstral coefficient (SFCC) based feature extraction method has been used successfully. The conventional method for extraction of such feature uses triangular filter banks similar to mel frequency cepstral coefficient (MFCC) features. In this work, we first present Gaussian filter based speech-signalbased frequency cepstral coefficient (GSFCC) based feature extraction technique derived from the speech-signal-based scale. Next, we use spectral entropy modulated speech-signal-based scale along with Gaussian filter to derive new cepstral feature for ASV task. We find improved performance of proposed features over baseline method in both clean and noisy conditions. The experiments were carried on NIST SRE 2001 database with simulated noise. In addition, a more recent real-world, noisy database VoxCeleb1 is used in the study. Finally, we do score level fusion of auditory filter based cepstral feature with datadriven filter based cepstral features. The performance obtained by the proposed features gives up to 15.71% and 15.37% relative improvement in equal error rate (EER) over the baseline method in NIST SRE and VoxCeleb1 databases, respectively.

Susanta Kumar Sarangi

Papers

Optimization of data-driven filterbank for automatic speaker verification

A novel approach in feature level for robust text-independent speaker identification system

Improved Speech-Signal Based Frequency Warping Scale for Cepstral Feature in Robust Speaker Verification System

Gaussian Filter Based Data-Driven Cepstral Features for Robust Speaker Verification System