Conventional Mel Frequency Cepstral Coefficients Research Articles

Home Activity Recognition by Sounds of Daily Life Using Improved Feature Extraction Method

In recent years, with the aging of society, many kinds of research have been actively conducted to recognize human activity in a home to watch over the elderly. Multiple sensors for activity recognition are used. However, we need to consider privacy when using these sensors. One of the candidates of the sensors that keep privacy is a sound sensor. MFCC (Mel-Frequency Cepstral Coefficient) is widely used as a feature extraction algorithm for voice recognition. However, it is not suitable to apply conventional MFCC to activity recognition by sounds of daily life. We denote “sounds of daily life” as “life sounds” simply in this paper. The reason is that conventional MFCC does not extract well several features of life sounds that appear at high frequencies. This paper proposes the improved MFCC and reports the evaluation results of activity recognition by machine learning SVM (Support Vector Machine) using features extracted by improved MFCC.

Wavelet-based Parametric Feature Subset Selection for Speaker and Accent Recognition using Genetic Algorithm

Research on speaker and accent recognition studies using the Malay language in the field of Automatic Speech Recognition (ASR) is limited, with most studies focusing on speech recognition. This study proposes to increase the performance Malaysian speakers and accent recognition using wavelets transform, namely Wavelet Packet Transform (WPT) and Dual-Tree Complex Wavelet Packet Transform (DT-CWPT). A variety of feature extraction combinations, including conventional Mel Frequency Cepstral Coefficients (MFCC), Linear Predictive Coding (LPC) and wavelets transform, were implemented to compare the effectiveness of the proposed method. Although the proposed approach resulted in improved detection rate, it faced challenges in terms of high feature dimensionality and increased computation time. To address these issues, the Genetic Algorithm (GA) approach has been adopted to reduce the number of irrelevant features, accelerate the learning system and achieve better performance. The extracted features were trained using various classifiers, including k-Nearest Neighbors (k-NN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The experimental results showed that the best speaker recognition accuracy was 97.33% for English numbers using SVM classifier and 96.02% for Malay words using the ELM classifier with a combination of wavelets, LPC and MFCC features. For accent recognition, the ELM classifier yielded the best performance, achieving 95.28% accuracy for English numbers with a combination of wavelets and MFCC features and 96.72% for Malay words using combined feature extraction of wavelets, LPC and MFCC feature extraction. It can be concluded that Malay words yielded better recognition rates than English numbers. Furthermore, use of GA effectively reduced the overall number of features while maintaining high accuracy level.

Frequency domain analysis of MFCC feature extraction in children’s speech recognition system

Abstract —The research on speech recognition systems currently focuses on the analysis of robust speech recognition systems. When the speech signals are combined with noise, the recognition system becomes distracted, struggling to identify the speech sounds. Therefore, the development of a robust speech recognition system continues to be carried out. The principle of a robust speech recognition system is to eliminate noise from the speech signals and restore the original information signals. In this paper, researchers conducted a frequency domain analysis on one stage of the Mel Frequency Cepstral Coefficients (MFCC) process, the Fast Fourier Transform (FFT), in children's speech recognition system. The FTT analysis in the feature extraction process determined the effect of frequency value characteristics utilized in the FFT output on the noise disruption. The analysis method was designed into three scenarios based on the value of the employed FFT points. The differences between scenarios were based on the number of shared FFT points. All FFT points were divided into four, three, and two parts in the first, second, and third scenarios, respectively. This study utilized children's speech data from the isolated TIDIGIT English digit corpus. As comparative data, the noise was added manually to simulate real-world conditions. The results showed that using a particular frequency portion following the scenario designed on MFCC affected the recognition system performance, which was relatively significant on the noisy speech data. The designed method in the scenario 3 (C1) version generated the highest accuracy, exceeded the accuracy of the conventional MFCC method. The average accuracy in the scenario 3 (C1) method increased by 1% more than all the tested noise types. Using various noise intensity values (SNR), the testing process indicates that scenario 3 (C1) generates a higher accuracy than conventional MFCC in all tested SNR values. It proves that the selection of specific frequency utilized in MFCC feature extraction significantly affects the recognition accuracy in a noisy speech.

Effect of audio pre-processing technique for neural network on lung sound classification

In this paper, we intended to study the effect of two audio pre-processing methods to multilayer feedforward network performance on lung sound classification problem. The first one is Mel Frequency Cepstral Coefficients (MFCC) and the second is the MFCC supplement with Linear Discriminant Analysis (LDA). Datasets applied in this study came from Kaggle Respiratory Sound Database, which is the largest resource for machine learning. As a result, MFCC supplement with LDA has both good performance and significant improvement than conventional MFCC.

Glottal features for classification of phonation type from speech and neck surface accelerometer signals

Glottal source characteristics vary between phonation types due to the tension of laryngeal muscles with the respiratory effort. Previous studies in the classification of phonation type have mainly used speech signals recorded by microphone. Recently, two studies were published in the classification of phonation type using neck surface accelerometer (NSA) signals. However, there are no previous studies comparing the use of the acoustic speech signal vs. the NSA signal as input in classifying phonation type. Therefore, the current study investigates simultaneously recorded speech and NSA signals in the classification of three phonation types (breathy, modal, pressed). The general goal is to understand which of the two signals (speech vs. NSA) is more effective in the classification task. We hypothesize that by using the same feature set for both signals, classification accuracy is higher for the NSA signal, which is more closely related to the physical vibration of the vocal folds and less affected by the vocal tract compared to the acoustical speech signal. Glottal source waveforms were computed using two signal processing methods, quasi-closed phase (QCP) glottal inverse filtering and zero frequency filtering (ZFF), and a group of time-domain and frequency-domain scalar features were computed from the obtained waveforms. In addition, the study investigated the use of mel-frequency cepstral coefficients (MFCCs) derived from the glottal source waveforms computed by QCP and ZFF. Classification experiments with support vector machine classifiers revealed that the NSA signal showed better discrimination of the phonation types compared to the speech signal when the same feature set was used. Furthermore, it was observed that the glottal features showed complementary information with the conventional MFCC features resulting in the best classification accuracy both for the NSA signal (86.9%) and the speech signal (80.6%).

When Old Meets New: Emotion Recognition from Speech Signals

Speech is one of the most natural communication channels for expressing human emotions. Therefore, speech emotion recognition (SER) has been an active area of research with an extensive range of applications that can be found in several domains, such as biomedical diagnostics in healthcare and human–machine interactions. Recent works in SER have been focused on end-to-end deep neural networks (DNNs). However, the scarcity of emotion-labeled speech datasets inhibits the full potential of training a deep network from scratch. In this paper, we propose new approaches for classifying emotions from speech by combining conventional mel-frequency cepstral coefficients (MFCCs) with image features extracted from spectrograms by a pretrained convolutional neural network (CNN). Unlike prior studies that employ end-to-end DNNs, our methods eliminate the resource-intensive network training process. By using the best prediction model obtained, we also build an SER application that predicts emotions in real time. Among the proposed methods, the hybrid feature set fed into a support vector machine (SVM) achieves an accuracy of 0.713 in a 6-class prediction problem evaluated on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset, which is higher than the previously published results. Interestingly, MFCCs taken as unique input into a long short-term memory (LSTM) network achieve a slightly higher accuracy of 0.735. Our results reveal that the proposed approaches lead to an improvement in prediction accuracy. The empirical findings also demonstrate the effectiveness of using a pretrained CNN as an automatic feature extractor for the task of emotion prediction. Moreover, the success of the MFCC-LSTM model is evidence that, despite being conventional features, MFCCs can still outperform more sophisticated deep-learning feature sets.

A Pitch and Noise Robust Keyword Spotting System Using SMAC Features with Prosody Modification

Spotting of keywords in continuous speech signal with the aid of the computer is called a keyword spotting (KWS) system. A variety of strategies have been suggested in the literature to detect keywords from the adult’s speech effectively. However, only a limited number of studies have been reported for KWS in children’s speech. Due to the difference in physiological properties, the pitch and speaking rate of children’s differ from the adult’s. Consequently, KWS system model parameters trained on the speech data from adult’s signal yield poor performance for children speech. In this paper, we have developed a KWS system for spotting keywords from children’s speech using models trained on adults’ speech. The proposed approach uses spectral moment time–frequency distribution augmented by low-order cepstral (SMAC) as the front-end feature. The mismatches due to differences in pitch and speaking rate of children and adult speakers are further mitigated by data-augmented training using explicit pitch and speaking rate modifications. The experimental findings presented in this paper show that the SMAC feature offers significantly better output for both clean and noisy test conditions than the conventional Mel frequency cepstral coefficients.

Recognition of emotion from speech using evolutionary cepstral coefficients

An optimal representation of acoustic features is an ongoing challenge in automatic speech emotion recognition research. In this study, we proposed Cepstral coefficients based on evolutionary filterbanks as emotional features. It is difficult to guarantee that an individual optimized filterbank provides the best representation for emotion classification. Consequently, we employed six HMM-based binary classifiers that used a specific filterbank, which was optimized by a genetic algorithm to categorize the data into seven emotion classes. These optimized classifiers were applied in a hierarchical manner and outperformed conventional Mel Frequency Cepstral Coefficients in terms of overall emotion classification accuracy. The proposed method using evolutionary-based Cepstral coefficients achieved a weighted average recall of 87.29% on the Berlin database while the same approach but using conventional Cepstral features achieved only 79.63%.

Speech emotion recognition using cepstral features extracted with novel triangular filter banks based on bark and ERB frequency scales

Speech emotion recognition (SER) refers to the process of recognizing the emotional state of the speaker from the speech utterance. In earlier studies, wide varieties of cepstral features have been proposed to develop SER systems. The mel-frequency cepstral coefficients (MFCC), and human-factor cepstral coefficients (HFCC) are two popularly used variants of cepstral features. MFCC and HFCC features are extracted from speech signals using mel and human-factor filter banks, respectively. The magnitude response of individual filters in these filter banks is triangular in shape. As a result, these filter banks are referred to as triangular filter banks (TFB) and the corresponding extracted cepstral coefficients can be denoted as TFBCC-M (in case of MFCC) and TFBCC-HF (in case of HFCC). The mel-filter bank (TFB-M) is constructed using mel-scale, while the human-factor filter bank (TFB-HF) is constructed using human factor scale, which is a combination of mel and equivalent rectangular bandwidth (ERB) scales. Similarly, different frequency scales can be used to realize different TFBs to extract different types of TFBCC features. In this direction, this paper proposes two new TFBs denoted as TFB-B and TFB-E, realized using bark and ERB scales to extract new cepstral features referred to as TFBCC-B and TFBCC-E, respectively. The mathematical background to construct the proposed TFB-B and TFB-E is presented. The proposed filter banks are used along with the conventional TFB-M and TFB-HF to extract four different types of TFBCC features. These features are extracted from the emotional speech signals of two databases, namely Berlin database of emotional speech (Emo-DB) and Surrey audio-visual expressed emotion speech database (SAVEE). The extracted features are used to develop speaker-dependent (SD) and speaker-independent (SI) based SER systems using support vector machines. The performance of the respective features is analyzed in terms of isolated and combined usage. The experimental results show that the cepstral features extracted using the proposed TFBs are effective in characterizing and recognizing emotions similar to conventional MFCC and HFCC features. Moreover, the combined use of different cepstral features have resulted to improve the overall recognition performance of SER systems. In case of Emo-DB database, isolated use of the proposed TFBCC-B and TFBCC-E features achieve recognition accuracies of 83.23% and 81.99% for SD scenario, and 75% and 60.94% for SI scenario, respectively. Similarly, for SAVEE database, the recognition accuracies of 75% and 66.67% for SD scenario, and 44.17% and 55% for SI scenario are achieved. In case of Emo-DB database, the maximum recognition accuracies of 86.96% (for different combinations of conventional and proposed features namely, TFBCC-{(M+E), (M+B+E), (HF+B+E), (M+HF+B+E)}) and 77.08% (for combination TFBCC-(M+B+E)) are achieved for SD and SI scenarios, respectively. Similarly, for SAVEE database, the maximum recognition accuracies of 77.08% (for combination TFBCC-(M+HF+E)), and 55.83% (for combination TFBCC-(B+E)) are achieved for SD and SI scenarios, respectively.

Mixture linear prediction Gammatone Cepstral features for robust speaker verification under transmission channel noise

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%.

Analysis and Detection of Pathological Voice Using Glottal Source Features

Automatic detection of voice pathology enables objective assessment and earlier intervention for the diagnosis. This study provides a systematic analysis of glottal source features and investigates their effectiveness in voice pathology detection. Glottal source features are extracted using glottal flows estimated with the quasi-closed phase (QCP) glottal inverse filtering method, using approximate glottal source signals computed with the zero frequency filtering (ZFF) method, and using acoustic voice signals directly. In addition, we propose to derive mel-frequency cepstral coefficients (MFCCs) from the glottal source waveforms computed by QCP and ZFF to effectively capture the variations in glottal source spectra of pathological voice. Experiments were carried out using two databases, the Hospital Universitario Príncipe de Asturias (HUPA) database and the Saarbrücken Voice Disorders (SVD) database. Analysis of features revealed that the glottal source contains information that discriminates normal and pathological voice. Pathology detection experiments were carried out using support vector machine (SVM). From the detection experiments it was observed that the performance achieved with the studied glottal source features is comparable or better than that of conventional MFCCs and perceptual linear prediction (PLP) features. The best detection performance was achieved when the glottal source features were combined with the conventional MFCCs and PLP features, which indicates the complementary nature of the features.

Detection of Nasalized Voiced Stops in Cleft Palate Speech Using Epoch-Synchronous Features

The presence of velopharyngeal dysfunction in individuals with cleft palate CP nasalizes the voiced stops. Due to this, voiced stops /b/, /d/, /g/ tend to be perceive like nasal consonants /m/, /n/, /ng/. In this work, a novel algorithm is proposed for the detection of nasalized voiced stops in CP speech using epoch-synchronous features. Speech regions corresponding to consonant and consonant-vowel transitions are segmented using the knowledge of glottal activity, syllable nucleus, low-frequency spectral dominance, and vowel onset point. The segmented regions are epoch-synchronously processed to analyze the spectral, spectro-temporal, excitation source, and periodicity characteristics of normal and nasalized voiced stops. Spectral and spectro temporal features are computed using single pole filter based time-frequency representation. The amplitude of Hilbert envelope of linear prediction residual, measured around the epoch is used to analyze the effect of nasalization on excitation source. Comparison of speech frames of successive inter-epoch intervals is carried out to analyze the periodicity characteristics. The proposed features are used to develop a support vector machine classifier for the classification of normal and nasalized voiced stops. Segmentation accuracy for the proposed knowledge based method is found to be better than the hidden Markov model based force-alignment approach. The detection rate of nasalized voiced stops is found to be high for the proposed epoch synchronous features than the conventional Mel-frequency cepstral coefficients.

Investigating Text-Independent Speaker Verification Systems Under Varied Data Conditions

This work makes an investigation into speaker verification (SV) from the view of practical systems. Limited data SV is preferred in order to have user comfort and effective decision delivery for regular usage. However, reduction in speech data affects the SV performance that becomes a concern for field deployment. In this work, varied data conditions for SV are explored, and sufficient train with limited test data is presented as a preferable anatomy for practical systems. Different explorations are made from the perspective of improving performance in varied data conditions. These explorations include vocal tract constriction feature to include speaker-specific acoustic–phonetic information, different attributes of voice source features that carry alternative/complementary information from that carried by conventional mel-frequency cepstral coefficient features. Further, kernel discriminant analysis is performed at the back end of i-vector-based speaker modeling for channel/session compensation that is found to work well for varied data conditions. Finally, a framework is proposed in combination with the stated explorations to have a better speaker characterization, which is more effective in case of sufficient train and limited test data scenario. The proposed framework achieves significant improvement in performance [equal error rate (EER): 11.20%, detection cost function (DCF): 0.1990], compared to the baseline (EER: 22.31%, DCF: 0.4128) for sufficient train with 2-s test segment case, showing scope toward application-oriented systems.

Addressing noise and pitch sensitivity of speech recognition system through variational mode decomposition based spectral smoothing

In this paper, we propose a novel front-end speech parameterization technique for automatic speech recognition (ASR) that is less sensitive towards ambient noise and pitch variations. First, using variational mode decomposition (VMD), we break up the short-time magnitude spectrum obtained by discrete Fourier transform into several components. In order to suppress the ill-effects of noise and pitch variations, the spectrum is then sufficiently smoothed. The desired spectral smoothing is achieved by discarding the higher-order variational mode functions and reconstructing the spectrum using the first-two modes only. As a result, the smoothed spectrum closely resembles the spectral envelope. Next, the Mel-frequency cepstral coefficients (MFCC) are extracted using the VMD-based smoothed spectra. The proposed front-end acoustic features are observed to be more robust towards ambient noise and pitch variations than the conventional MFCC features as demonstrated by the experimental evaluations presented in this study. For this purpose, we developed an ASR system using speech data from adult speakers collected under relatively clean recording conditions. State-of-the-art acoustic modeling techniques based on deep neural networks (DNN) and long short-term memory recurrent neural networks (LSTM-RNN) were employed. The ASR systems were then evaluated under noisy test conditions for assessing the noise robustness of the proposed features. To assess robustness towards pitch variations, experimental evaluations were performed on another test set consisting of speech data from child speakers. Transcribing children's speech helps in simulating an ASR task where pitch differences between training and test data are significantly large. The signal domain analyses as well as the experimental evaluations presented in this paper support our claims.

Intelligibility assessment of cleft lip and palate speech using Gaussian posteriograms based on joint spectro-temporal features.

Intelligibility is considered as one of the primary measures for speech rehabilitation of individuals with a cleft lip and palate (CLP). Currently, speech processing and machine-learning-based objective methods are gaining more research interest as a way to quantify speech intelligibility. In this work, joint spectro-temporal features computed from a time-frequency representation of speech are explored to derive speech representations based on Gaussian posteriograms. A comparative framework using dynamic time warping (DTW) is used to quantify the intelligibility of child CLP speech. The DTW distance is used to score sentence-level intelligibility and tested for correlation with perceptual intelligibility ratings obtained from expert speech-language pathologists. A baseline DTW system using the conventional Mel-frequency cepstral coefficients (MFCCs) is also developed to compare the performance of the proposed system. Spearman's rank correlation coefficient between the objective intelligibility scores and the perceptual intelligibility rating is studied. A Williams significance test is conducted to assess the statistical significance of the correlation difference between the methods. The results show that the system based on joint spectro-temporal features significantly outperforms the MFCC-based system.

Exploring Text-Constraint Models and Source Information for Long-Enrollment with Short-Test Speaker Verification

This work focuses on long-enrollment with short-test speaker verification (SV) from the perspective of application-oriented systems. The importance of phonetic match between train and test models is explored in terms of having a text-constraint model-based framework on Part IV of RedDots database. This database has a text-dependent and a text-prompted-based enrollment conditions for speaker modeling. Two different text-constraint setups are formalized for evaluating the effect of text match on train and test sessions. Further, the excitation source features mel power difference of spectrum in subbands, residual mel frequency cepstral coefficient and discrete cosine transform of integrated linear prediction residual are investigated to determine their significance for text-constraint-based framework. Although the source features individually perform poorer compared to the conventional mel frequency cepstral coefficient (MFCC) features, their significance is reflected in fusion due to the complementary nature of information carried by them. Additionally, the source features become imperative for text-constraint-based models for long-enrollment with short-test SV in fusion to MFCC features and achieves commendable improvement from baseline framework of text-prompted-based enrollment condition. This thus minimizes the performance difference between text-dependent and text-prompted-based enrollment condition showing importance of text-constraint models and source information in long-enrollment with short-test-based framework favorable from the perspective of field deployable systems.

Robust feature extraction and uncertainty estimation based on attractor dynamics in cyclic deep denoising autoencoders

Because the input and the output values of the deep denoising autoencoders (DDAs) have the same representation space, the output values of a DDA can be used as its input values, which leads to a repeatable cycle for DDA. Using this fact, in this paper we proposed cyclic deep denoising autoencoders (CDDAs). Moreover, the proposed CDDA was exploited for noise robust bottleneck feature extraction. Considering the CDDA as an attractor network, we analyzed its attractor dynamics and proposed an uncertainty estimation method for the bottleneck features. The capabilities of the proposed feature extraction method are: eliminating the need for labeled training data, improving the robustness of bottleneck features of DDAs without excessive training, providing a new uncertainty estimation trend, and being applicable in other speech processing tasks like speaker recognition. Our proposed feature extraction method was evaluated on the Aurora-2 robust speech recognition task and compared it to conventional Mel frequency cepstral coefficients (MFCC) and autoencoder bottleneck features. The proposed method resulted in up to 8% improvement in word recognition accuracy relative to MFCC features.

Acoustic analysis of misarticulated trills in cleft lip and palate children.

In this paper, acoustic analysis of misarticulated trills in cleft lip and palate speakers is carried out using excitation source based features: strength of excitation and fundamental frequency, derived from zero-frequency filtered signal, and vocal tract system features: first formant frequency (F1) and trill frequency, derived from the linear prediction analysis and autocorrelation approach, respectively. These features are found to be statistically significant while discriminating normal from misarticulated trills. Using acoustic features, dynamic time warping based trill misarticulation detection system is demonstrated. The performance of the proposed system in terms of the F1-score is 73.44%, whereas that for conventional Mel-frequency cepstral coefficients is 66.11%.

Studying the role of pitch-adaptive spectral estimation and speaking-rate normalization in automatic speech recognition

In the context of automatic speech recognition (ASR) systems, the front-end acoustic features should not be affected by signal periodicity (pitch period). Motivated by this fact, we have studied the role of pitch-synchronous spectrum estimation approach, referred to as TANDEM STRAIGHT, in this paper. TANDEM STRAIGHT results in a smoother spectrum devoid of pitch harmonics to a large extent. Consequently, the acoustic features derived using the smoothed spectra outperform the conventional Mel-frequency cepstral coefficients (MFCC). The experimental evaluations reported in this paper are performed on speech data from a wide range of speakers belonging to different age groups including children. The proposed features are found to be effective for all groups of speakers. To further improve the recognition of children's speech, the effect of vocal-tract length normalization (VTLN) is studied. The inclusion of VTLN further improves the recognition performance. We have also performed a detailed study on the effect of speaking-rate normalization (SRN) in the context of children's speech recognition. An SRN technique based on the anchoring of glottal closure instants estimated using zero-frequency filtering is explored in this regard. SRN is observed to be highly effective for child speakers belonging to different age groups. Finally, all the studied techniques are combined for effective mismatch reduction. In the case of children's speech test set, the use of proposed features results in a relative improvement of 21.6% over the MFCC features even after combining VTLN and SRN.

Constructing accurate and robust HMM/GMM models for an Arabic speech recognition system

Conventional Hidden Markov Model (HMM) based Automatic Speech Recognition (ASR) systems generally utilize cepstral features as acoustic observation and phonemes as basic linguistic units. Some of the most powerful features currently used in ASR systems are Mel-Frequency Cepstral Coefficients (MFCCs). Speech recognition is inherently complicated due to the variability in the speech signal which includes within- and across-speaker variability. This leads to several kinds of mismatch between acoustic features and acoustic models and hence degrades the system performance. The sensitivity of MFCCs to speech signal variability motivates many researchers to investigate the use of a new set of speech feature parameters in order to make the acoustic models more robust to this variability and thus improve the system performance. The combination of diverse acoustic feature sets has great potential to enhance the performance of ASR systems. This paper is a part of ongoing research efforts aspiring to build an accurate Arabic ASR system for teaching and learning purposes. It addresses the integration of complementary features into standard HMMs for the purpose to make them more robust and thus improve their recognition accuracies. The complementary features which have been investigated in this work are voiced formants and Pitch in combination with conventional MFCC features. A series of experimentations under various combination strategies were performed to determine which of these integrated features can significantly improve systems performance. The Cambridge HTK tools were used as a development environment of the system and experimental results showed that the error rate was successfully decreased, the achieved results seem very promising, even without using language models.