Sharif Digital Repository

This paper proposes a Distant Speech Recognition system based on a novel speaker Localization and Beamforming (SRLB) algorithm. To localize the speaker an algorithm based on Steered Response Power by utilizing harmonic structures of speech signal is proposed. This new scheme has the ability of speaker verification by fundamental frequency variation: therefore it can be utilized in the design of a speech recognition system for verified speakers. Then the performance of the Farsi speech recognition engine is evaluated under notorious conditions of noise and reverberation. Simulation results and tests on real data shows that by utilizing proposed localization scheme, recognition accuracy... 

A Comprehensive speech database is one of the important tools for developing speech recognition systems; these tools are necessary for telephony recognition, too. Although adequate databases for direct speech recognizers exist, there is not an appropriate database for telephony speech recognizers. Most methods suggested for solving this problem are based on building new databases which tends to consume much time and many resources; or they used a filter which simulates circuit switch behavior to transform direct databases to telephony ones, in this case resulted databases have many differences with real telephony databases. In this paper we introduce a framework for creating telephony speech... 

With increasing use of computers in everyday life, improved communication between machines and human is needed. To make a right communication and understand a humankind face which is made in a graphical environment, implementing the audio and visual projects like lip reading, audio and visual speech recognition and lip making are needed. Lack of a complete audio and visual database for this application in Farsi language made us provide a new complete Farsi database for this project that is called SFAVD. It is a unique audio and visual database which in addition to considering Farsi conceptual and speech structure, it considers influence of speech on lip changes. This database is created for... 

This paper proposes a set of higher-order modified moments for estimation of the fundamental frequency of speech and explores the impact of the speech window length on pitch estimation error. The pitch extraction methods are evaluated in a range of noise types and SNRs. For calculation of errors, pitch reference values are calculated from manually-corrected estimates of the periods obtained from laryngograph signals. The results obtained for the 3 rd and 4 th order modified moment compare well with methods based on correlation and magnitude difference criteria and the YIN method; with improved pitch accuracy and less occurrence of large errors  

Isolated Word Recognition (IWR) is becoming increasingly attractive due to the improvement of speech recognition techniques. However, the accuracy of IWR suffers when large databases or words with similar pronunciation are used. The criterion for accurate speech recognition is suitable segmentation. However, the traditional method of segmentation equal segmentation does not produce the most accurate result. Furthermore, utilizing manual segmentation based on events is not possible in large databases. In this paper, we introduce an intelligent segmentation based on Hierarchical Temporal Decomposition (HTD). Based on this method, a temporal decomposition (TD) algorithm can be used to... 

Pitch detection is the process of determining the period of the vocal cords closure or in another word the time duration of one glottal closed, open and returning phase. Dyadic wavelets transform (DyWT) and modified higher order moment, which is based on the autocorrelation function, are two pitch detection methods. DyWT is an accurate pitch detection method, however it has less accuracy compared to modified higher order moment. On the other hand modified higher order moment has high computational complexity and is time consuming. In this paper, we propose a pitch detection method based on DyWT which has use modified higher order moment. Modified higher order moment is applied only in some... 

In this paper, a problem in spoken dialogue systems namely the menu problem, is introduced and solved by a POMDP model. To overcome the large size of the menu problem, a new method for achieving an optimal policy called divided POMDP method is introduced. Conditions for the problem to be solved by the proposed method are specified and the problem properties resulting in the given conditions are presented. The proposed method is evaluated using a typical menu problem with different menu sizes and it is shown that this method is superior to the conventional methods such as FRTDP for the problems it is capable to solve. Moreover, it converges faster in getting to an optimal policy  

The problem of signal detection using a flexible and general model is considered. Owing to applicability and flexibility of sparse signal representation and approximation, it has attracted a lot of attention in many signal processing areas. In this study, the authors propose a new detection method based on sparse decomposition in a union of subspaces model. Their proposed detector uses a dictionary that can be interpreted as a bank of matched subspaces. This improves the performance of signal detection, as it is a generalisation for detectors. Low-rank assumption for the desired signals implies that the representations of these signals in terms of some proper bases would be sparse. Their... 

There has been no isolated word recognition database for the Persian language so far. In this paper we introduce FarsName dataset which contains 20 thousands isolated-word Persian utterances spoken by 226 speakers from all regions of the country each saying an average of 88 Persian names. There is a total of 5235 unique names in this dataset. Various cell phone brands have been used to record this dataset. This indicates the high diversity of the utterances in this dataset. We have been able to achieve 10.34% WER on this set using Kaldi. This is a very good performance considering the recording environment have been normal and potentially noisy. © 2017 IEEE  

In this paper, we introduce a new dataset for SAD and evaluate certain common methods such as GMM, DNN, and RNN on it. We have collected our dataset in a semi-supervised approach, using subtitled movies, with a labeling accuracy of 95%. This semi-automatic method can help us collect huge amounts of labeled audio data with very high diversity in language, speaker, and channel. We model the problem of SAD as a classification task to two classes of speech and non-speech. When using GMM for this problem, we use two separate mixtures to model speech and non-speech. In the case of neural networks, we use a softmax layer at the end of the network, with two neurons which represent speech and... 

In this paper, a frame-based method with reference frame was proposed to recognize six basic facial emotions (anger, disgust, fear, happy, sadness and surprise) and also neutral face. By using face landmarks, a fast algorithm was used to calculate an appropriate descriptor for each frame. Furthermore, Linear Discriminant Analysis (LDA) was used to reduce the dimension of defined descriptors and to classify them. The LDA problem was solved using the least squares solution and Ledoit-Wolf lemma. The proposed method was also compared with some studies on CK+ dataset which has the best accuracy among them. To generalize the proposed method over CK+ dataset, a landmark detector was needed.... 

In this paper we have reviewed Nevisa Persian speech recognition engine. Nevisa is an HMM-based, large vocabulary speaker-independent continuous speech recognition system. Like most successful recognition systems, MFCC with some modification has been used as speech signal features. It also utilizes a VAD based on signal energy and zero-crossing rate. Maximum likelihood estimation criterion the core of which are the classical segmental k-means and Baum-Welsh algorithms is used for training the acoustic models. The system is based on phoneme modeling and utilizes synchronous beam search based on lexicon tree for decoding the acoustic utterances. Language modeling for Persian has been... 

In this paper we present a novel approach for adjusting a multi band spectral subtraction filter coefficients based on speech recognition system results. Currently most speech enhancement techniques are designed according to various waveform level criteria such as maximizing SNR or minimizing signal error. However improvement in these criteria does not necessarily result in increasing speech recognition performance. Only if these methods generate sequence of features that maximize or increase the likelihood of the correct transcription relative to other incorrect competing hypotheses, speech recognition performance will increase. Here we use an utterance with a known transcription and... 

Appropriate sensors are a crucial necessity for the success of recognition systems. Nature has always coevolved sensors and recognition systems and this can also be done in artificially intelligent systems. To get a very fast isolated word speech recognition system for a small embedded speech recognizer, an evolutionary approach has been used to create together the required sensors and appropriate recognition structures. The input sensors are designed and evolved through inspiration by the human auditory system and the classification is done by artificial neural networks. The resulting system is compared with a widely used speech recognition system, and the results are quite satisfactory. ©... 

Voice activity detection is an important step in some speech processing systems, such as speech recognition, speech enhancement, noise estimation, speech compression ... etc. In this paper a new voice activity detection algorithm based on wavelet transform is proposed. In this algorithm we use the energy in each sub band, and by two methods we extract feature vector from these values. Experimental results demonstrate advantage over different VAD methods. ©2008 IEEE