Chip design of MFCC extraction for speech recognition

This paper introduces a new approach to control and drive the DC motor, using speech recognition. The speech signal can be provided through microphone that is connected to computer. A DC motor connected through microcontroller can be driven in forward or reverse direction at different speeds, as well as it can be stopped by giving speech command. In this paper Mel Frequency Cepstral Co-efficient (MFCC) is used to recognize the user's speech and vector quantization is used to increase speech recognition accuracy. The MFCC and VQ algorithm, for speech recognition have been implemented in MATLAB 7.5 version on Windows Vista platform, and the supporting hardware setup is being implemented.

The mel frequency cepstral coefficients (MFCC) is one of the mast important features, which is required among various kinds of speech applications. The chip for speech features extraction based on the MFCC algorithm is first proposed. The chip is designed with area efficient consideration and can achieve the following: (1) the reduction of table size and multiplication complexity by means of the symmetric property of the cosine function, (2) the decrease of the multiplication load and required constant memory in the calculation of the weighted energy spectrum by applying the mapping relationship between the mel scale and the frequency scale, (3) the minimization of the look-up table size for logarithm operations by modifying the partitioned table look-up method. The chip is fabricated with 0.6 /spl mu/m double-metal CMOS technology. It contains approximately 10,000 gates occupying 3.2/spl times/3.3 mm/sup 2/ area and the maximum clock rate is 50 MHz.

The extraction of representative characteristics from voice signals, is the basic step for any application where speech signal processing is performed. In this paper, a comparative analysis of the Mel frequency cepstral coefficients, extracted from samples of voiced and silent speech is exposed. The classical methodology for extracting Mel frequency cepstral coefficient is evaluated to show the implementation steps and the robustness of such kind of features representing in a unique form the statistical and representative information of time varying signals. To achieve this comprehensive evaluation, first, a contextualization in theoretical terms of the methodology is performed to show the mathematical basis for the coefficient extraction. Each step of the process is analyzed in terms of the representative information of the coefficients. Therefore an evaluation in comparative terms against the previous developments for data interpretation is done. The coefficients are extracted using the classical implementation of the Mel frequency cepstral coefficient feature extraction process in a free software framework implemented in Matlab®. The results show the robustness of the Mel frequency cepstral coefficient algorithm in the characterization of voiced and silent speech signals, which represents an advantage for the construction of any kind of speech processing systems.

—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.

Performance evaluation of Hindi speech recognition system using optimized filterbanks

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)

Mel frequency cepstral coefficient (MFCC) is a popular feature extraction method for a speech recognition system. However, this method is susceptible to noise even though it generates a high accuracy. The conventional MFCC method has a degraded performance when the input signal has noises. This paper presents the implementation of denoising wavelet on speech input of MFCC feature extraction method. The addition of denoising process using wavelet transformation was expected to improve the MFCC performance on noisy signals. The study used 120 speech data, with 30 data were used as the reference, and the other 90 were used as the testing data. The testing data were mixed with white Gaussian noise and then tested to the speech recognition system that already had the reference data. Parameters used in the wavelet denoising process were soft thresholding with the Minimaxi thresholding rule. Eleven wavelet methods on decomposition level 10 were tested on the denoising process. The classification process used K-nearest neighbor (KNN) method. The Fejer-Korovkin 6 wavelet was the best denoising speech signal method that achieved the highest accuracy on input signals with SNR of 5-15dB. Meanwhile, the Daubechies 5 method had a high accuracy on input signal with SNR of 3 dB. All of the tested denoising methods using wavelet transformation were able to improve the accuracy of the speech recognition system on input signals with SNR of 0-10 dB compared to the system without denoising method.

Speech production knowledge has been used to enhance the phonetic representation and the performance of automatic speech recognition (ASR) systems successfully. Representations of speech production make simple explanations for many phenomena observed in speech. These phenomena can not be easily analyzed from either acoustic signal or phonetic transcription alone. One of the most important aspects of speech production knowledge is the use of articulatory knowledge, which describes the smooth and continuous movements in the vocal tract. In this paper, we present a new articulatory model to provide available information for rescoring the speech recognition lattice hypothesis. The articulatory model consists of a feature front-end, which computes a voicing feature based on a spectral harmonics correlation (SHC) function, and a back-end based on the combination of deep neural networks (DNNs) and hidden Markov models (HMMs). The voicing features are incorporated with standard Mel frequency cepstral coefficients (MFCCs) using heteroscedastic linear discriminant analysis (HLDA) to compensate the speech recognition accuracy rates. Moreover, the advantages of two different models are taken into account by the algorithm, which retains deep learning properties of DNNs, while modeling the articulatory context powerfully through HMMs. Mandarin speech recognition experiments show the proposed method achieves significant improvements in speech recognition performance over the system using MFCCs alone.

This paper addresses the development of an Automatic Speech Recognition (ASR) system for the Moroccan Dialect. Dialectal Arabic (DA) refers to the day-to-day vernaculars spoken in the Arab world. In fact, Moroccan Dialect is very different from the Modern Standard Arabic (MSA) because it is highly influenced by the French Language. It is observed throughout all Arab countries that standard Arabic widely written and used for official speech, news papers, public administration and school but not used in everyday conversation and dialect is widely spoken in everyday life but almost never written. we propose to use the Mel Frequency Cepstral Coefficient (MFCC) features to specify the best speaker identification system. The extracted speech features are quantized to a number of centroids using vector quantization algorithm. These centroids constitute the codebook of that speaker. MFCC’s are calculated in training phase and again in testing phase. Speakers uttered same words once in a training session and once in a testing session later. The Euclidean distance between the MFCC’s of each speaker in training phase to the centroids of individual speaker in testing phase is measured and the speaker is identified according to the minimum Euclidean distance. The code is developed in the MATLAB environment and performs the identification satisfactorily.

In this paper, we analyze the impact of five Arabic dialects on the front-end and pronunciation dictionary components of an Automatic Speech Recognition (ASR) system. We use ASR's phonetic decision tree as a diagnostic tool to compare the robustness of MFCC and MLP front-ends to dialectal variations in the speech data and found that MLP Bottle-Neck features are less robust to such variations. We also perform a rule-based analysis of the pronunciation dictionary, which enables us to identify dialectal words in the vocabulary and automatically generate pronunciations for unseen words. We show that our technique produces pronunciations with an average phone error rate 9.2%. Arabic language is characterized by its multitude of dialects. Although Modern Standard Arabic (MSA) is used in writing, TV/radio broadcasts and for formal communication, all informal communication is typically carried out in one of the regional dialects of Arabic. Dialectal variations influence the pronunciation dictionary, acoustic and language models in an ASR. Previous works on dialectal Arabic ASR include cross- dialectal data sharing (1), improved pronunciation and language modeling (2, 3), etc. In this paper, we describe our experiments on a dialectal Arabic speech database, where we focus on analyzing the behavior of different front-ends and pronunciation dictionary due to dialectal variations between speakers. We evaluate Mel-Frequency Cepstral Coefficients (MFCC) and Multi-Layer Perceptrons (MLP), on their ability to handle these variations that arise due to different dialects. Extending our previous work on gender normalization (4), we use phonetic decision trees as a diagnostic tool to analyze the influence of dialect in the clustered models. We introduce questions pertaining to dialect in addition to context in the building of the decision tree. We then build the tree to cluster the contexts and calculate the number of leaves that belong to branches with dialectal questions. The ratio of such 'dialectal' models to the total model size is used as a measure for dialect normalization. The higher the ratio, the more models are affected by the dialect, hence less normalization and vice versa. We further extend our analysis to the pronunciation dictionary, where we investigate ways to generate rule-based pronunciations for unseen words in a dialect with minimum manual effort. Our setup features a 'Pan-Arabic' dictionary, which contains pronunciations typically found in five Arabic dialects. We analyze the pronunciation variants in our common dictionary using acoustic model alignments to derive the dialect-specific pronunciations for each word. This forms the source of our rule-learning algorithm which maps word pronunciations from one dialect to another. These rules are then used to generate pronunciations for unseen words and the accuracy is estimated.

Combined speech enhancement and auditory modelling for robust distributed speech recognition

Speech recognition can be defined as the process of converting voice signals into the ranks of the word, by applying a specific algorithm that is implemented in a computer program. The research of speech recognition in Indonesia is relatively limited. This paper has studied methods of feature extraction which is the best among the Linear Predictive Coding (LPC) and Mel Frequency Cepstral Coefficients (MFCC) for speech recognition in Indonesian language. This is important because the method can produce a high accuracy for a particular language does not necessarily produce the same accuracy for other languages, considering every language has different characteristics. Thus this research hopefully can help further accelerate the use of automatic speech recognition for Indonesian language. There are two main processes in speech recognition, feature extraction and recognition. The method used for comparison feature extraction in this study is the LPC and MFCC, while the method of recognition using Hidden Markov Model (HMM). The test results showed that the LPC method is better than MFCC in Indonesian language speech recognition.

Multi-microphone speech recognition integrating beamforming, robust feature extraction, and advanced DNN/RNN backend

This paper describes a study of a set of features based on cepstral mean and variance normalization (CMVN) plus auto regressive moving average (ARMA) filtering technique which is called MVA and on histogram equalization (HEQ) for robust speech recognition. First, we use MVA then HEQ in combination with CMVN and ARMA filtering as a post-processing module to mel frequency cepstral coefficients (MFCC), Relative Spectral-Perceptual linear prediction (RASTA-PLP) and power normalized cepstral coefficients (PNCC) features to improve the performance of the automatic speech recognition (ASR) system. The results on the Arabic database task have shown that both methods MVA and HEQ+ARMA improves the success rate for all features compared to the baseline system however HEQ was not found to perform better than MVA. The results also provide that RASTA-PLP outperforms PNCC and MFCC features.

The Mel-frequency cepstral coefficients (MFCC) are commonly used in speech recognition systems. But, they are highly sensitive to presence of external noise. In this paper, we propose a two-step method to compensate noise effects on MFCC. In the first step, we propose a sub-band SNR-dependent compression function for Mel sub-band energies to give higher weights to sub-bands less contaminated with noise and give lower weights to sub-bands more contaminated with noise. In the second step, we apply temporal filters to the weighted MFCCs in order to improve their temporal characteristics. Our results on Aurora2 databases show that the proposed method has higher performance than both of conventional temporal filtering methods and weighted MFCC.