Text Independent Amharic Language Speaker Identification in Noisy Environments using Speech Processing Techniques

A statistically designed Automatic Speech Recognition (ASR) system extracts features from speech signals using feature extraction methods, links the extracted features with the expected phonetics of the hypothesis using acoustic models, uses language model to add prior information about the structure of the target language. For many years, Mel-frequency Cepstral coefficients (MFCC), n-gram, and Hidden Markov Model (HMM) approaches have been used predominantly for feature extraction, language modeling and acoustic modeling, respectively. However, performance degradation of MFCC in noisy conditions and inaccuracy of HMMs while handling large vocabularies have made researchers to propose more efficient methods. The proposed work uses noise robust method Gammatone Frequency Cepstral Coefficients(GFCC) for feature extraction, trigram language modeling, and HMM-Gaussian mixture model (GMM) based acoustic modeling to implement a continuous Hindi language ASR system. Also, it applies Differential Evolution (DE) technique to refine the GFCC features and discriminative techniques to enhance performance of the acoustic model. The performance of the implemented system has been evaluated by using different feature extraction methods, variants of n-gram language modeling techniques and different discriminative techniques in clean as well as noisy conditions. Initially, the results reveal that DE optimized GFCC with HMM-Gaussian Mixture Model (GMM) acoustic modeling performs better than MFCC, PLP and MF-PLP feature extraction methods. Secondly, the experimental results show that the Minimum Phone Error (MPE) outperforms Maximum Mutual Information (MMI) and Maximum Likelihood Estimation (MLE) and trigram based language modeling gives more accurate results than unigram and bigram language modeling. Finally, it has been concluded that the continuous Hindi language ASR system implemented using DE refined GFFC feature extraction method with MPE discriminative training technique and trigram based language modeling gives better accuracy in clean as-well-as noisy environments.

Aiming at the problem of inaccurate event location in noisy environment by existing acoustic even detection technology, this paper presents an acoustic event detection algorithm based on two-stage judgement. Firstly, the acoustic events existing in the audio signal are located by the two-stage judgement detection method, both of the distance of the Mel Frequency Cepstral Coefficients (MFCC) and the short-time energy between each audio signal frame and the noise average are calculated, respectively. The MFCC distance in the frequency domain which can produce fine but incomplete results is the first judgement; the energy distance in the time domain is the second judgement, which is used to supplement the first judgment. Studies have shown that the Gammatone filter bank is biologically closer to the human ear structure than the Mel filter bank. The Gammatone Frequency Cepstral Coefficients (GFCC) of the detected acoustic events were then extracted. The detected acoustic events are classified by the Gaussian Mixture Model (GMM). By analyzing the experimental results, the algorithm can solve the problem that the sound feature information is insufficient and the noise segment boundary is not clear. This system is more suitable for the situation where a variety of acoustic events should be analyzed.

Automatic Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with a wide range of applications. The purpose of speech emotion recognition system is to automatically classify speaker's utterances into different emotional states such as disgust, boredom, sadness, neutral, and happiness. The speech samples in this paper are from the Berlin emotional database. Mel Frequency cepstrum coefficients (MFCC), Linear prediction coefficients (LPC), linear prediction cepstrum coefficients (LPCC), Perceptual Linear Prediction (PLP) and Relative Spectral Perceptual Linear Prediction (Rasta-PLP) features are used to characterize the emotional utterances using a combination between Gaussian mixture models (GMM) and Support Vector Machines (SVM) based on the Kullback-Leibler Divergence Kernel. In this study, the effect of feature type and its dimension are comparatively investigated. The best results are obtained with 12-coefficient MFCC. Utilizing the proposed features a recognition rate of 84% has been achieved which is close to the performance of humans on this database.

Performance evaluation of Hindi speech recognition system using optimized filterbanks

With the wide application of electronic computers and artificial intelligent machines, people find that the best way of communication between man and machine is voice communication. Nowadays, highly accurate speaker identification systems are required. Speaker-specific characteristics exist in speech signals due to different speakers having different resonances of the vocal tract. In this paper, Mel-Frequency Cepstrum Coefficient (MFCC) is used to extract speakers features, and Gaussian Mixture Model (GMM) is established for the speakers, Maximum Likelihood Estimate (MLE) is use to estimate the model parameters of GMM. The aim is to identify the Cantonese speaker quickly and accurately in the noisy environment. Experimental results indicate that the method is very effective under noisy environments.

In this paper, we present a Mixture Linear Prediction based approach for robust Gammatone Cepstral Coefficients extraction (MLPGCCs). The proposed method provides performance improvement of Automatic Speaker Verification (ASV) using i-vector and Gaussian Probabilistic Linear Discriminant Analysis GPLDA modeling under transmission channel noise. The performance of the extracted MLPGCCs was evaluated using the NIST 2008 database where a single channel microphone recorded conversational speech. The system is analyzed in the presence of different channel transmission noises such as Additive White Gaussian (AWGN) and Rayleigh fading at various Signals to Noise Ratio (SNR) levels. The evaluation results show that the MLPGCCs features are a promising way for the ASV task. Indeed, the speaker verification performance using the MLPGCCs proposed features is significantly improved compared to the conventional Gammatone Frequency Cepstral Coefficients (GFCCs) and Mel Frequency Cepstral Coefficients (MFCCs) features. For speech signals corrupted with AWGN noise at SNRs ranging from (-5 dB to 15 dB), we obtain a significant reduction of the Equal Error Rate (EER) ranging from 9.41% to 6.65% and 3.72% to 1.50%, compared with conventional MFCCs and GFCCs features respectively. In addition, when the test speech signals are corrupted with Rayleigh fading channel we achieve an EER reduction ranging from 23.63% to 7.8% and from 10.88% to 6.8% compared with conventional MFCCs and GFCCs, respectively. We also found that the combination of GFCCs and MLPGCCs gives the highest performance of speaker verification system. The best performance combination achieved is around EER from 0.43% to 0.59% and 1.92% to 3.88%.

From the past years, researchers have showed a very interest in the speech recognition systems based on the emotions. Mainly the research has been done with the aim to bring closer both human and computer with each other by recognizing mood swings. In the recognizing process we must know how to represent the emotions on the basis of some features like sad, happy etc. When the verbal content is well recognized on the speaker's emotion, a promising enhancement of such systems would come. So recognition in speech is a crucial step. In the proposed work speech emotions will be recognized using hybridization of GFCC (Gammatone Frequency Cepstral Coefficients) and BPNN (Back Propagation Neural Network). The whole simulation will be done in the MATLAB environment to check the comparison result.

In recent years, crime prevention systems have been developed to detect various hazardous situations. In general, the systems utilize the image information recorded by a camera to monitor the situations. It is however difficult to detect them in the blind area. To address the problem, it is required to utilize not only image information but also acoustic information occurred in such situations. Our previous study showed that two acoustic features including rahmonic and mel-frequency cepstrum coefficients (MFCCs) are effective for detecting the shouted speech. Rahmonic shows a subharmonic of fundamental frequency in the cepstrum domain, and MFCCs represent coefficients that collectively make up mel-frequency cepstrum. In this method, a shouted speech model is constructed from these features by using a gaussian mixture model (GMM). However, the previous method with GMM has difficulty in representing temporal changes of the speech features. In this study, we further expand the previous method using hidden Markov model (HMM) which has state transition to represent the temporal changes. Through objective experiments, the proposed method using HMM could achieve higher detection performance of the shouted speech than the conventional method using GMM.

Traditional coal-gangue recognition methods usually do not consider the impact of equipment noise, which severely limits its adaptability and recognition accuracy. This paper mainly studies the more accurate recognition of coal-gangue in the noise site environment with the operation of shearer, conveyor, transfer machine and other device in the process of top coal caving. Mel Frequency Cepstrum Coefficients (MFCC) smoothing method was introduced to express the intrinsic feature of sound pressure more clearly in the coal-gangue recognition site. Then, a multi-branch convolution neural network (MBCNN) model with three branches was developed, and the smoothed MFCC feature was incorporated into this model to realize the recognition of falling coal and gangue in noisy environment. The sound pressure signal datasets under the operation of different device were constructed through a great deal of laboratory and site data acquisition. Comparative experiments were carried out on noiseless dataset, single noise dataset and simulated site dataset, and the results show that our method can provide higher correct recognition accuracy and better robustness. The proposed coal-gangue recognition approach based on MBCNN and MFCC smoothing can not only recognize the state of falling coal or gangue, but also recognize the operational state of site device.

The model-based speech enhancement method usually models types of noises in a prior and selects one noise model in the enhancement phase. In this paper, we study the modeling and selection (i.e. noise identification) algorithm for the model-base speech enhancement. For the noise signal features, Mel Frequency Cepstrum Coefficient (MFCC), Liner Prediction Coefficient (LPC), Linear Spectral Frequency (LSF) and Linear Predictive Cepstrum Coefficients (LPCC) are utilized. For the noise model, codebook, Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) are adopted. We couple these features and models and provide a comparative performance analysis of them. In the experiment, we use NOIZEUS database for noise modeling and testing. The experiment results show that MFCC and GMM is an excellent couple for noise identification, and LFS can take the place of LPC in the codebook-based speech enhancement.

A method with a combination of multi-dimensional fusion features and a modified deep neural network (MFF-MDNN) is proposed to recognize underwater acoustic targets in this paper. Specifically, due to the complex and changeable underwater environment, it is difficult to describe underwater acoustic signals with a single feature. The Gammatone frequency cepstral coefficient (GFCC) and modified empirical mode decomposition (MEMD) are developed to extract multi-dimensional features in this paper. Moreover, to ensure the same time dimension, a dimension reduction method is proposed to obtain multi-dimensional fusion features in the original underwater acoustic signals. Then, to reduce redundant features and further improve recognition accuracy, the Gaussian mixture model (GMM) is used to modify the structure of a deep neural network (DNN). Finally, the proposed underwater acoustic target recognition method can obtain an accuracy of 94.3% under a maximum of 800 iterations when the dataset has underwater background noise with weak targets. Compared with other methods, the recognition results demonstrate that the proposed method has higher accuracy and strong adaptability.

This paper describes the development of an efficient speech recognition system using different techniques such as Mel Frequency Cepstrum Coefficients (MFCC), Vector Quantization (VQ) and Hidden Markov Model (HMM).This paper explains how speaker recognition followed by speech recognition is used to recognize the speech faster, efficiently and accurately.MFCC is used to extract the characteristics from the input speech signal with respect to a particular word uttered by a particular speaker.Then HMM is used on Quantized feature vectors to identify the word by evaluating the maximum log likelihood values for the spoken word.

Speaker recognition is an Artificial Intelligent (AI) technology that lets the machine to process, interpret and respond to human language. In this work, the recorded speech developed from a collection of audio speeches is used as a database. Mel-frequency cepstral coefficients (MFCC) and gammatone frequency cepstral coefficients (GFCC) are two different cepstral features used in this work. These extracted features are then used to train, validate and test the classifier. Support Vector Machine (SVM) is the classifier used in developing the speaker identification system. This classifier is trained to classify the input speech into one of the ethnicity classes: Malay, Chinese, Indian or Bumiputera. The results are based on the two different usages of cepstral features from the same speech utterances by speakers. Finally, the comparative analysis of the speaker identification system is made concerning features and classifier. The results revealed that a combination of GFCC and pitch as the feature vectors (Model 4) produced the highest accuracy rate of 86.1%.

This paper proposes a new method to detect and identify foreign matter mixed in a plastic bottle filled with transfusion solution. A spin-stop mechanism and mixed illumination style are applied to obtain high contrast images between moving foreign matter and a static transfusion background. The Gaussian mixture model is used to model the complex background of the transfusion image and to extract moving objects. A set of features of moving objects are extracted and selected by the ReliefF algorithm, and optimal feature vectors are fed into the back propagation (BP) neural network to distinguish between foreign matter and bubbles. The mind evolutionary algorithm (MEA) is applied to optimize the connection weights and thresholds of the BP neural network to obtain a higher classification accuracy and faster convergence rate. Experimental results show that the proposed method can effectively detect visible foreign matter in 250-mL transfusion bottles. The misdetection rate and false alarm rate are low, and the detection accuracy and detection speed are satisfactory.

Oral pronunciation refers to the way words are spoken aloud, encompassing aspects like accent, intonation, and clarity. It plays a crucial role in effective communication, influencing listeners to understand the speaker. The purpose of this research is to construct an English oral pronunciation evaluation model based on a deep learning algorithm. In this research, we propose a novel Dung Beetle Optimization-driven Customized Deep Belief Network (DBO-CDBN) for accurate detection and evaluation of English oral pronunciation. We obtain a dataset that comprises audio recordings of English speech, including both correct and mispronounced samples to train and evaluate the suggested pronunciation evaluation model. Data normalization is used to pre-process the gathered raw audio data, it enhances the overall quality of the data. The Gammatone Frequency Cepstral Coefficients (GFCC) algorithm is employed to extract the crucial features from the obtained audio data. In our proposed model, the DBO optimization algorithm iteratively finetunes the CDBN architecture to enhance the evaluation accuracy. The approach, which is executed in python, is shown to be more successful in pronunciation evaluation and to provide prospective advances in the area when its performance is examined and compared to other approaches in terms of precision (96.78%), recall (93.65%), and [Formula: see text]-measure (94.53%). The established model is implemented using Python software. During the outcome analysis phase, we evaluate our model’s performance across various parameters. In addition, we also performed a comparative analysis using diverse existing methodologies. The findings demonstrate the excellence and effectiveness of the suggested future economic forecasting model.