Khasi Speech Recognition using Hidden Markov Model with Different Spectral Features: A Comparison

In view of the growing use of automatic speech recognition in the modern society, we study various alternative representations of the speech signal that have the potential to contribute to the improvement of the recognition performance. In this paper wavelet based features using different wavelets are used for Hindi digits recognition. The recognition performance of these features has been compared with Linear Prediction Coefficients (LPC) and Perceptual Linear Prediction (PLP) features. All features have been tested using Hidden Markov Model (HMM) based classifier for speaker independent Hindi digits recognition. The recognition performance of PLP features is11.3% better than LPC features. The recognition performance with db10 features has shown a further improvement of 12.55% over PLP features. The recognition performance with db10 is best among all wavelet based features.

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.

Background/Objectives: Speech produced by individuals with hearing loss differs notably from that of normal-hearing (NH) individuals. Although cochlear implants (CIs) provide sufficient auditory input to support speech acquisition and control, there remains considerable variability in speech intelligibility among CI users. As a result, speech produced by CI talkers often exhibits distinct acoustic characteristics compared to that of NH individuals. Methods: Speech data were obtained from eight cochlear-implant (CI) and eight normal-hearing (NH) talkers, while electroencephalogram (EEG) responses were recorded from 11 NH listeners exposed to the same speech stimuli. Support Vector Machine (SVM) classifiers employing 3-fold cross-validation were evaluated using classification accuracy as the performance metric. This study evaluated the efficacy of Support Vector Machine (SVM) algorithms using four kernel functions (Linear, Polynomial, Gaussian, and Radial Basis Function) to classify speech produced by NH and CI talkers. Six acoustic features—Log Energy, Zero-Crossing Rate (ZCR), Pitch, Linear Predictive Coefficients (LPC), Mel-Frequency Cepstral Coefficients (MFCCs), and Perceptual Linear Predictive Cepstral Coefficients (PLP-CC)—were extracted. These same features were also extracted from electroencephalogram (EEG) recordings of NH listeners who were exposed to the speech stimuli. The EEG analysis leveraged the assumption of quasi-stationarity over short time windows. Results: Classification of speech signals using SVMs yielded the highest accuracies of 100% and 94% for the Energy and MFCC features, respectively, using Gaussian and RBF kernels. EEG responses to speech achieved classification accuracies exceeding 70% for ZCR and Pitch features using the same kernels. Other features such as LPC and PLP-CC yielded moderate to low classification performance. Conclusions: The results indicate that both speech-derived and EEG-derived features can effectively differentiate between CI and NH talkers. Among the tested kernels, Gaussian and RBF provided superior performance, particularly when using Energy and MFCC features. These findings support the application of SVMs for multimodal classification in hearing research, with potential applications in improving CI speech processing and auditory rehabilitation.

ABSTRACTThe goal of this study is to present a comparative analysis between the three feature extraction techniques: Linear Predictive Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC), and Weighted Linear Prediction Cepstral Coefficients (WLPCC) for distinguishing speech of Intellectually Disabled (ID) children from Typically Developed (TD). Speech samples of ID children were recorded from a government-owned special school in India and used for the analysis. Control samples were taken from the author’s institute. Pre-processing techniques were used for better parameterization. Two classifiers, Artificial Neural Network (ANN) and Linear Discriminant Analysis , were applied to classify between disordered and the normal speech. Cross-validation technique was used to evaluate the reliability of the classifier. This study has discussed the effect on the classification accuracy using different values of frame size, percentage of frame overlapping, α value of pre-emphasis first-order filter and the order p. The experimental calculation interpreted that all the three parameterization techniques can be used for the correct classification of the ID speech from controls and WLPCC features slightly outperform than LPCC and considerably outperforms than LPC features.

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.

In this paper, a mode decomposition analysis-based adaptive approach is proposed to provide high diagnostic performance for automated voice pathology detection systems. The aim of the study is to develop a reliable and effective system using adaptive cepstral domain features derived from the empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete empirical mode decomposition with adaptive noise (CEEMDAN) methods. The descriptive feature sets are obtained by applying mel-frequency cepstral coefficients (MFCCs) and their derivatives, linear predictive coefficients (LPCs) and linear predictive cepstral coefficients (LPCCs) techniques to each decomposition level. The class-balanced data are generated on the VOice ICar fEDerico II database samples using the synthetic minority oversampling technique (SMOTE). The ReliefF algorithm is used to select the most effective and distinctive features. A combination of selected features and a support vector machine (SVM) classifier is used to identify pathological voices. In the pathology detection approach, the results show that the cepstral features based on EMD and SVM-cubic achieves the highest performance with 99.85\% accuracy, 99.85\% F1-score and 0.997 Matthews correlation coefficient (MCC). In pathology-type classification, the cepstral features based on EEMD and SVM-quadratic approach provided the highest performance with 96.49\% accuracy, 96.46\% F1 and 0.949 MCC values. The comprehensive results of this study reveal that mode decomposition-based approaches are more successful and effective than traditional methods for detection and classification of pathological voices.

The selection of the proper feature extraction method is an essential issue for any Automatic Speech Recognition system. This has been conducted in order to choose the suitable feature extraction method for Arabic phoneme recognition system based Malay speakers. In this paper, the implementation of three feature extraction methods involves Frequency Cepstral Coefficients (MFCC), Linear Predictive Coefficients (LPC) and Perceptual Linear Prediction (PLP) has been done. And each feature extraction method is applied on Arabic phonemes in two cases the first case is; the signal is noisy and the second case is the signal is enhanced. The phoneme signals enhancement is achieved using wiener filter. Feed-Forward Neural Network is implemented as a recognizer. The outcome of this study shows that proposed system can give the highest recognition rate with MFCC. The recognition rate is 95.3% and 98.12% in the case of noisy phoneme signal and enhanced phoneme signal respectively. The evaluation and testing the feature extraction methods were based on Arabic phonemes corpus has collected from Malay speakers.

LPC-based formant enhancement method in Kalman filtering for speech enhancement

Automatic speech recognition (ASR) system is used to recognize the text transcript from the given speech signal. Such speech signal can contain either isolated words or large vocabulary continuous speech (LVCS). Isolated words can be recognized with high accuracy in clean environment, but recognizing continuous words involves various parameters like speech corpus, speaker, environment noise, etc., that affects the accuracy of automatic speech recognition system directly. In the proposed work, hybrid feature extraction technique combines the perceptual linear predictive (PLP) and mel frequency cepstral coefficient (MFCC) to improve the accuracy of ASR in noisy environment. Voice activity and detection (VAD)-based frame dropping is used for improving the phonemes modeling by removing the pauses and distorted elements from the given speech signal. The proposed hybrid model with VAD is implemented by using self-generated speech corpus and shows relatively 12% increase in recognition rate compared with the state-of-the-art methodology.KeywordsAutomatic speech recognition (ASR)Perceptual linear predictive (PLP)Mel frequency cepstral coefficient (MFCC)Voice activity and detection (VAC)

Artificial neural networks (ANNs), in combination with parametric spectral representation techniques, are applied for the detection of helicopter sound. Training of the ANN detectors was based on simulated helicopter sound from four helicopters and a variety of nonhelicopter sounds. Coding techniques based on linear prediction coefficients (LPCs) have been applied to obtain spectral estimates of the acoustic signals. Other forms of the LPC parameters such as reflection coefficients, cepstrum coefficients, and line spectral pairs (LSPs) have also been used as feature vectors for the training and testing of the ANN detectors. We have also investigated the use of wavelet transform for signal de-noising prior to feature extraction. The performance of various feature extraction techniques is evaluated in terms of their detection accuracy.

Output-based speech quality (OBQ) refers to objective speech quality assessment using only received speech without utilizing the input speech record. This paper proposes three new OBQ measures and evaluates their performance. Parameters derived from perceptual linear prediction (PLP) coefficients are used to provide speaker independence required by the objective measures. PLP, PLP cepstrum, and PLP delta-cepstrum parameters are computed for output speech records from an undistorted source speech database and vector quantized. The resulting codebook provides a reference for computing objective distance measures for distorted speech. The proposed objective measures are the transition probability distance, the median minimum distance, and the chi-squared distance. The OBQ parameters are tested on four different speech datasets, and correlation is computed between subjective scores and objective distances under a variety of conditions. The results indicate that the proposed algorithms are robust against speaker, text, and distortion variation.

A sonar trainer requires a wide variety of broadband background noises to create realistic sounds and displays. Linear prediction techniques are typically used for speech. This paper describes how linear prediction coefficient (LPC) techniques were used to simulate a wide variety of underwater background noises for trainers. First the spectral and spatial background noise models are developed. Then the application of a single channel LPC synthesizer for generating background noise is described, along with test results.

A low-cost acoustic road-type classification system is proposed to be used in road-tyre friction force estimation in active safety applications. The system employs audio signal processing and extracts features such as linear predictive coefficients (LPC), mel-frequency cepstrum coefficients (MFCC) and power spectrum coefficients (PSC). The features are extracted using time windows of 0.02, 0.05 and 0.1 seconds in order to find the best representative window for the signal properties which should also be as short as possible for active safety systems. In order to find the best feature space, a variance analysis based approach is considered to represent the road types as distinguished classes. Optimised feature space is classified using artificial neural networks (ANN). The results show that the designed ANN can classify the road types with 91% accuracy at worst condition. To demonstrate the value of the system, a case study including traction control application is reported.

Several interpolation techniques of linear prediction coefficients (LPC) for speech signal coding were experimentally analyzed. The obtained results show that direct interpolation of LPC can be applied for coding and decoding of significantly long segments of voiced speech. The best performance in sense of least number of unstable filters, smallest spectral distortions and greatest MOS score, was achieved when for interpolation were used linear and Hermite polynomials. Higher order interpolation polynomials such us cubic or third order splines, produced significant alternations in obtained LPC that led to worst results of the quality of the decoded speech. Also higher order polynomials without additional post processing of interpolated LPC produced more unstable filters. Ill. 5, bibl. 11 (in English; summaries in English, Russian, Lithuanian).

The performance of multitaper perceptual linear prediction (PLP) features of speech samples to discriminate healthy and early stage Parkinson diseased subjects is investigated in this paper. The PLP features are conventionally obtained by computing the power spectrum using a single tapered Hamming window. This estimated spectrum exhibits large variance which can be reduced by computing the weighted average of power spectra obtained using a set of tapered windows, leading to multitaper spectral estimation. In this investigation, two multitaper techniques namely Sine wave taper and Thomson multitaper along with the conventional single taper windowing are investigated. Artificial Neural network is then used to classify the PLP features extracted by applying the three types of window tapers on the speech signals of healthy and early stage Parkinson affected people and their respective performances are compared. The results show more accuracy using the multitaper techniques when compared with the conventional single taper technique. It is seen that the accuracy obtained using Sine wave tapers as well as Thomson multitaper is maximum for five tapers. An improvement in the recognition accuracy by 7.5% using the Sine tapers and by 6.9% using the Thomson tapers is obtained when compared with the conventional method. An improvement in other performance measures like Equal error rate, False positive rate, False negative rate, Sensitivity and Specificity is also observed in the multitaper techniques.