A Comparative Study of Recognition of Speech Using Improved MFCC Algorithms and Rasta Filters

In this paper, based on the study of two speech recognition algorithms, two designs of speech recognition system are given to realize this isolated speech recognition mobile robot control system based on ARM9 processor. The speech recognition process includes pretreatment of speech signal, characteristic extrication, pattern matching and post-processing. Mel-Frequency cepstrum coefficients (MFCC) and linear prediction cepstrum coefficients (LPCC) are the two most common parameters. Through analysis and comparison the parameters, MFCC shows more noise immunity than LPCC, so MFCC is selected as the characteristic parameters. Both dynamic time warping (DTW) and hidden markov model (HMM) are commonly used algorithm. For the different characteristics of DTW and HMM recognition algorithm, two different programs were designed for mobile robot control system. The effect and speed of the two speech recognition system were analyzed and compared.

유도 전동기는 항공 산업, 자동차 산업 등의 산업 현장에서 중요한 역할을 하고 있으며, 이러한 유도 전동기의 고장으로 인한 피해를 최소화하기 위해 유도 전동기의 고장 검출 및 분류 시스템의 개발이 중요한 문제로 대두되고 있다. 이에 본 논문에서는 정상 및 각종 비정상 상태의 유도 전동기 진동 신호에 대해 부분 자기 상관(partial autocorrelation, PARCOR) 계수, 로그 스펙트럼 파워(log spectrum powers, LSP), 캡스트럼 계수의 평균값(cepstrum coefficients mean, CCM), 멜 주파수 캡스트럼 계수(mel-frequency cepstrum coefficient, MFCC)의 네 가지 특징 벡터를 신경 회로망의 입력으로 사용하여 유도 전동기의 고장을 검출하고 분류하였다. 고장 분류를 위한 최적의 특징 벡터를 찾기 위해 추출하는 특징의 수를 2에서 20으로 바꾸어 가며 분류 성능을 평가한 결과 CCM을 제외한 나머지의 경우 5~6의 특징만으로 분류 정확도가 거의 100%에 가까운 결과를 보였다. 또한 본 논문에서는 실제 산업 현장에서 진동 신호 취득 시 포함될 수 있는 잡음을 고려하여 취득한 신호에 백색 잡음(white Gaussian noise)을 인위적으로 추가하여 실험한 결과 LSP, PARCOR, MFCC 순으로 잡음 환경에 강인한 특징 벡터임을 확인할 수 있었다. Induction motors play a vital role in aeronautical and automotive industries so that many researchers have studied on developing a fault detection and classification system of an induction motor to minimize economical damage caused by its fault. With this reason, this paper extracts robust feature vectors from the normal/abnormal vibration signals of the induction motor in noise circumstance: partial autocorrelation (PARCOR) coefficient, log spectrum powers (LSP), cepstrum coefficients mean (CCM), and mel-frequency cepstrum coefficient (MFCC). Then, we classified different types of faults of the induction motor by using the extracted feature vectors as inputs of a neural network. To find optimal feature vectors, this paper evaluated classification performance with 2 to 20 different feature vectors. Experimental results showed that five to six features were good enough to give almost 100% classification accuracy except features by CCM. Furthermore, we considered that vibration signals could include noise components caused by surroundings. Thus, we added white Gaussian noise to original vibration signals, and then evaluated classification performance. The evaluation results yielded that LSP was the most robust in noise circumstance, then PARCOR and MFCC followed by LSP, respectively.

This study investigates the effect of vowel-reduction dubbing practice on the Word Error Rate (WER) of automatic speech recognition (ASR)-generated captions in Zoom-based English presentations. Employing a convergent mixed-methods design, the research examines how misarticulation related to vowel reduction—particularly the realization of schwa—shapes ASR performance and caption accuracy. Twentyseven English teacher candidates completed structured dubbing tasks followed by ASR transcription analysis using Zoom’s auto-captioning system. WER was calculated using standard computational formulae, and both quantitative data (percentages, accuracy ratios, and error rates) and qualitative data (error types, substitution patterns, and prosodic deviations) were systematically analyzed. Results indicate that substitution errors remained the most dominant ASR error type, especially when vowel reduction occurred without adequate prosodic support. A substantial improvement was observed following the intervention: the average WER decreased by approximately 52% (from 27.4% to 13.2%), demonstrating a strong effect of targeted dubbing practice on caption intelligibility. Participants who maintained clearer vowel quality and rhythmic timing achieved consistently lower WER scores, while those applying excessive or inconsistent vowel reduction produced higher rates of substitution and deletion errors. The study highlights the pedagogical value of integrating ASR-based feedback with dubbing activities to strengthen learners’ pronunciation intelligibility and schwa awareness. It further underscores the importance of TPACK-informed instruction that cultivates learners’ sensitivity to both human comprehensibility and machine recognition. Overall, these findings contribute to ongoing developments in pronunciation pedagogy by bridging technology-enhanced learning with EFL teacher education.

Desktop interaction solutions are often inappropriate for mobile devices due to small screen size and portability needs. Speech recognition can improve interactions by providing a relatively hands-free solution that can be used in various situations. While mobile systems are designed to be transportable, few have examined the effects of motion on mobile interactions. This paper investigates the effect of motion on automatic speech recognition (ASR) input for mobile devices. Speech recognition error rates (RER) have been examined with subjects walking or seated, while performing text input tasks and the effect of ASR enrollment conditions on RER. The obtained results suggest changes in user training of ASR systems for mobile and seated usage.

In this paper, we propose a new method for voice feature extraction by using a hierarchical clustering approach of inverse discrete cosine transform (IDCT) cepstrum coefficient. Since the IDCT cepstrum coefficient is transformed into the feature vector based on Euclidean metric, we call the proposed voice feature as “E-vector”. Comparing with other voice features, e.g. Mel frequency cepstrum coefficient (MFCC) and histogram of DCT cepstrum coefficients (HDCC), the E-vector can represent the dynamic characteristics and interactional relationships of the voice. In the numerical experiments, we use the speech files based on the voice of 630 people in TIMIT corpus, and employ Gaussian mixture model to compare the recognition accuracy of E-vector with that of MFCC and HDCC. The results show that E-vector outperformed other voice features on personal identification, and presented higher extensiveness on voice feature extraction.

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.

This paper compares the performance of two variants of the Particle Swarm Optimization (PSO) algorithm; PSO with constriction factor (PSO), and mutative PSO (MPSO) in optimizing Mel Frequency Cepstrum Coefficients (MFCC) parameters. The parameters were used to extract an optimal feature set for classifying healthy and hypothyroid infant cry using Multi-Layer Perceptrons (MLP). Specifically, the PSO variants optimize the number of filter banks and number of cepstrum coefficients in MFCC. Based on the values chosen by both PSO variants, the extracted features were then fed to a MLP classifier, which was trained to discriminate between the healthy and hypothyroid infant cry. Comparisons between the performance of PSO variants showed that MPSO managed to improve the convergence rate by 2.67% compared to PSO.

In this paper, we propose a joint optimal method for automatic speech recognition (ASR) and ideal binary mask (IBM) estimation in transformed into the cepstral domain through a newly derived generalized expectation maximization algorithm. First, cepstral domain missing feature marginalization is established using a linear transformation, after tying the mean and variance of non-existing cepstral coefficients. Second, IBM estimation is formulated using a generalized expectation maximization algorithm directly to optimize the ASR performance. Experimental results show that even in highly non-stationary mismatch condition (dance music as background noise), the proposed method achieves much higher absolute ASR accuracy improvement ranging from 14.69% at 0 dB SNR to 40.10% at 15 dB SNR compared with the conventional noise suppression method.

At present, most multi-dialect speech recognition models are based on a hard-parameter-sharing multi-task structure, which makes it difficult to reveal how one task contributes to others. In addition, in order to balance multi-task learning, the weights of the multi-task objective function need to be manually adjusted. This makes multi-task learning very difficult and costly because it requires constantly trying various combinations of weights to determine the optimal task weights. In this paper, we propose a multi-dialect acoustic model that combines soft-parameter-sharing multi-task learning with Transformer, and introduce several auxiliary cross-attentions to enable the auxiliary task (dialect ID recognition) to provide dialect information for the multi-dialect speech recognition task. Furthermore, we use the adaptive cross-entropy loss function as the multi-task objective function, which automatically balances the learning of the multi-task model according to the loss proportion of each task during the training process. Therefore, the optimal weight combination can be found without any manual intervention. Finally, for the two tasks of multi-dialect (including low-resource dialect) speech recognition and dialect ID recognition, the experimental results show that, compared with single-dialect Transformer, single-task multi-dialect Transformer, and multi-task Transformer with hard parameter sharing, our method significantly reduces the average syllable error rate of Tibetan multi-dialect speech recognition and the character error rate of Chinese multi-dialect speech recognition.

An audio-visual speech recognition system (AVSR) integrates audio and visual information to perform speech recognition task. The AVSR has various applications in practice especially in natural language processing systems such as speech-to-text conversion, automatic translation and sentiment analysis. Decades ago, researchers tend to use Hidden Markov Model (HMM) to construct speech recognition system due to its good achievements in success recognition rate. However, HMM's training dataset is enormous in order to have sufficient linguistic coverage. Besides, its recognition rate under noisy environments is not satisfying. To overcome this deficiency, a Recurrent Neural Network (RNN) based AVSR is proposed. The proposed AVSR model consists of three components: 1) audio features extraction mechanism, 2) visual features extraction mechanism and 3) audio and visual features integration mechanism. The features integration mechanism combines the output features from both audio and visual extraction mechanisms to generate final classification results. In this research, the audio features mechanism is modelled by Mel-frequency Cepstrum Coefficient (MFCC) and further processed by RNN system, whereas the visual features mechanism is modelled by Haar-Cascade Detection with OpenCV and again, it is further processed by RNN system. Then, both of these extracted features were integrated by multimodal RNN-based features-integration mechanism. The performance in terms of the speech recognition rate and the robustness of the proposed AVSR system were evaluated using speech under clean environment and Signal Noise Ratio (SNR) levels ranging from -20 dB to 20 dB with 5 dB interval. On average, final speech recognition rate is 89% across different levels of SNR.

We address the problem of estimating the word error rate (WER) of an automatic speech recognition (ASR) system without using acoustic test data. This is an important problem which is faced by the designers of new applications which use ASR. Quick estimate of WER early in the design cycle can be used to guide the decisions involving dialog strategy and grammar design. Our approach involves estimating the probability distribution of the word hypotheses produced by the underlying ASR system given the text test corpus. A critical component of this system is a phonemic confusion model which seeks to capture the errors made by ASR on the acoustic data at a phonemic level. We use a confusion model composed of probabilistic phoneme sequence conversion rules which are learned from phonemic transcription pairs obtained by leave-one-out decoding of the training set. We show reasonably close estimation of WER when applying the system to test sets from different domains.

Digit speech recognition is important in many applications such as automatic data entry, PIN entry, voice dialing telephone, automated banking system, etc. This paper presents speaker independent speech recognition system for Malayalam digits. The system employs Mel frequency cepstrum coefficient (MFCC) as feature for signal processing and Hidden Markov model (HMM) for recognition. The system is trained with 21 male and female voices in the age group of 20 to 40 years and there was 98.5% word recognition accuracy (94.8% sentence recognition accuracy) on a test set of continuous digit recognition task.

Mel-frequency Cesptral Coefficients (MFCC) and Predictive Linear Prediction (PLP) coefficients are two popular representations of continuous speech in existing Hidden Markov Model (HMM) based Automatic Speech Recognition (ASR) systems. Cepstral Mean Normalization (CMN) is often used as a post-processing step in the extraction of MFCC and PLP features to further enhance noise robustness at almost negligible computational cost. In this paper we build a closed dictionary, large vocabulary HMM-based Indonesian language ASR system using the CMU Sphinx in speech recognition toolkit implementing MFCC and PLP feature extraction, and CMN. We test the effect of various types and levels of noise on the word error rate (WER) of speech recognition. Utilizing CMN, an average improvement of 2% recognition over standard MFCC and PLP extraction methods is obtained at signal-to-noise ratios (SNR) below 24 decibels. A significant drop in recognition is observed between 12 and 6 dB SNR.

When utilizing the most likely state sequence (MLSS) criterion in Gauss mixture model-hidden Markov model (GMM-HMM) to acquire the best state series of observations, only the maximum likelihood state of speech frame is considered. Therefore, the influence of other states is neglected, which leads to the losing of some important information, and further reduces the recognition rate of the system. In this paper, we propose two new features, which are called state likelihood cluster feature (SLCF) and supervised state feature (SSF), to both reflect acoustic features and fuse state information. Combining SLCF and SSF with Mel frequency cepstrum coefficient (MFCC), Mel frequency cepstrum & state likelihood cluster feature (MSLCF) and Mel frequency cepstrum & supervised state feature (MSSF) are formed, respectively. By the proposed MSLCF and MSSF in Chinese speech recognition experiment, the relative error rate of the isolated word recognition system declines 6.10% and 9.66%, respectively, and the relative error rate of the continuous speech recognition system declines 2.53% and 11.05%, respectively.

In this paper a multi-channel speech enhancement framework for distant speech acquisition in noisy and reverberant environments for Non-negative Matrix Factorization (NMF)-based Automatic Speech Recognition (ASR) is proposed. The system is evaluated for its use in an assistive vocal interface for physically impaired and speech-impaired users. The framework utilises the Spatially Pre-processed Speech Distortion Weighted Multichannel Wiener Filter (SP-SDW-MWF) in combination with a postfilter to reduce noise and reverberation. Additionally, the estimation uncertainty of the speech enhancement framework is propagated through the Mel-Frequency Cepstrum Coefficients (MFCC) feature extraction to allow for feature compensation in a later stage. Results indicate that a) using a trade-off parameter between noise reduction and speech distortion has a positive effect on the recognition performance with respect to the well-known GSC and MWF and b) the addition of a postfilter and the feature compensation increases performance with respect to several baselines for a non-pathological and pathological speaker. Index Terms: multi-channel speech enhancement, speech recognition, uncertainty of estimation, dysarthric speech