남녀성별 분류를 위한 화자종속 음성인식 알고리즘

We conducted an investigation to explore how neurotypical (NT) listeners perceive the emotional tone of voice in sentences spoken by individuals with high-functioning autism spectrum disorders (ASD) and NT speakers. The investigation included both male and female speakers from both groups. In Study 1, NT listeners were asked to identify the emotional prosody (anger, fear, happiness, surprise or neutral) conveyed by the speakers. Results revealed that emotional expressions produced by male ASD speakers were generally less accurately recognized compared to male NT speakers. In contrast, emotions expressed by female ASD speakers were more accurately categorized compared to female NT speakers, except when expressing fear. This suggests that female ASD speakers may not express emotional prosody in the same way as their male counterparts. In Study 2, a subset of produced materials was rated for valence, voice modulation, and voice control to supplement Study 1 results: Female ASD speakers sounded less negative when expressing fear compared to female NT speakers. Male ASD speakers were perceived as less positive than NT speakers when expressing happiness. Voice modulation also differed between groups, showing a tendency for ASD speakers to follow different display rules for both positive emotions (happiness and surprise) tested. Finally, male ASD speakers were rated to use voice cues less appropriately compared to NT male speakers, an effect less pronounced for female ASD speakers. Together, the results imply that difficulties in social interactions among individuals with high-functioning ASD could be due to non-prototypical voice use of male ASD speakers and emphasize that female individuals do not show the same effects.

In this study, the morpheme of the dialogues in Korean textbooks for married immigrants was analyzed and the nouns in the dialogues were examined from the gender perspective. By comparing the dialogues between the pre-revised textbook 『Korean language for Married immigrant』 and the post-revised textbook 『Joyful Korean Language』 and 『Accurate Korean Language』, it was confirmed that there was a positive change between the two textbooks, but there were still parts that needed improvement. The speech ratio of male and female speakers improved, but the speech amount of female speakers still accounted for a large proportion. By analyzing the subject of the dialogue through the overall upper-frequency noun, the subject of the dialogue in the textbook after the revision was diversified. As a result of comparing the upper-frequency nouns according to gender, the bias of the conversation topic disappeared. However, the conversation on the subject of 'food' has become more biased than in the past, which can lead to stereotypes about gender roles, so it is suggested that it needs to be carefully reviewed and corrected. In addition, when noun vocabulary with markedness used by male and female speakers was listed in the order of upper-frequency, male speakers used more advanced words than female speakers in both textbooks before and after the revision. This seems to be due to the establishment of the social status and role of male and female speakers, and it was suggested that improvement was needed in that unequal perceptions were reflected in the setting of the social roles of male and female speakers.

Growth in Digital signal processing technology gives positives influences in human life. One of the branch of science that gives significant influence is digital speech processing. It can be expand into some applications that make human life easier. Digital speech processing is appropriate to speech recognition. Speech recognition is used to arrange the movement of remote control car. The remote control car will move according to our speech. This final project is closely related with speech recognition. The LPC (Linear Predictive Coding) method will extract the speech signal features and HMM (Hidden Markov Model) to modelling the speech signal are used. It is done by comparing model from extracting feature that is available in HMM modelling. Models will be used in the speech recognition process, if the models have highest level of conformity. The experiment has been done in two conditions, i.e ideal condition in room wih low noise level and unideal condition in room with noise. Result of the experiment from the whole sistem performace at ideal condition is 97,71% for people that have been inputed in the database, and 95,42% for people which have not been inputed in the database. Result of the experiment of database say it regulary is 97,14%. Result of the experiment of database at unideal condition is 54,28% for inputing word at noisy area, result of the experiment of database for inputing word at high frequency from siren voice is 98,57% and result of the experiment of inputing similar word with database is 97,6% Keywords: speech recognition, LPC, HMM

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.

본 논문에서는 먼저 신경회로망의 학습에 오차역전파 학습 알고리즘을 사용하여 각 프레임에서의 음성 및 잡음 구간의 검출에 의한 음성인식 알고리즘을 제안한다. 그리고 신경회로망에 의하여 음성 및 잡음 구간의 검출에 따라서 각 프레임에서 잡음을 제거하는 스펙트럼 차감법을 제안한다. 본 실험에서는 제안한 음성인식알고리즘의 성능을 원음성에 백색잡음 및 자동차 잡음을 부가하여 인식율을 평가한다. 또한 인식시스템에 의하여 검출된 음성 및 잡음 구간을 이용하여 각 프레임에서의 스펙트럼 차감법에 의한 잡음제거의 실험결과를 나타낸다. 잡음에 의하여 오염된 음성에 대하여 신호대잡음비를 사용하여 본 알고리즘이 유효하다는 것을 확인한다. This paper first proposes the speech recognition algorithm by detection of the speech and noise sections at each frame using a neural network training by back-propagation algorithm, then proposes the spectral subtraction method which removes the noises at each frame according to detection of the speech and noise sections. In this experiment, the performance of the proposed recognition system was evaluated based on the recognition rate using various speeches that are degraded by white noise and car noise. Moreover, experimental results of the noise reduction by the spectral subtraction method demonstrate using the speech and noise sections detecting by the speech recognition algorithm at each frame. Based on measuring signal-to-noise ratio, experiments confirm that the proposed algorithm is effective for the speech by corrupted the noise using signal-to-noise ratio.

Speech recognition is gaining an increasing research interest in the last five decades. Speech processing is considered as an interdisciplinary branch of electronics and computer science domain. It considers speech as an input and converts it into the corresponding text. This paper describes the design and development of Marathi Numeric Speech Dataset. Marathi Numbers (Ank) ranging from Shunya(0) to Nau(9) and are taken into consideration for recording. Speech samples are collected from 50 native and 50 non-native speakers of Marathi language. The dataset remains as a gender balanced since it is recorded from 50 females and 50 male speakers. The age of speakers will affect the speech. Therefore, 5 different age groups such as 11-20, 21-30, 31-40, 41-50 and 51-60 are considered. Native and non-native speakers are selected to obtain ample amount of variations in the pronunciation of Marathi numerals. Feature extraction and feature matching technique plays a vital role for speech recognition and here LPC (Linear Predictive Coding) is used for extracting features from samples, whereas ANN (Artificial neural network) is used to classify them. Experimental specifications and results are also discussed. This research work has attempted to design and develop a speech recognition system, which can understand Marathi Ank (Numbers) and identify them accurately.

Speakers from four age groups—sixth graders, university freshmen and sophomores, graduate teaching assistants, and people in their 50's and 60's—were audiotape recorded during spontaneous photograph‐elicited monologues, with 45‐second segments transcribed for later rating. University students read the anonymous transcripts and indicated their perceptions of the speakers on the Speech Dialect Attitudinal Scale. (SDAS). Data were of high reliability and consistent factor structure. Analyses of the three SDAS dimensions revealed: (1) no difference between male and female speakers on socio‐intellectual status; (2) female speakers rated substantially higher on aesthetic quality than male speakers, with the disparity greater in the two older groups of speakers; (3) on dynamism, male speakers rated higher than female speakers.

The authors explore the benefits of time-varying bit allocation to excitation and LPC (linear predictive coding) parameters for the case of codebook-excited LPC. The overall bit rate in the experiment was 4.8, 6.4, or 8.0 kb/s. In each case, permissible bit rates for the LPC component were 0, 24, 36, or 48 bits per frame, one of which was selected for each speech frame using a brute-force search maximum performance. Average SNR gains over conventional time-invariant methods were modest, on the order of 1 to 2 dB, but gains for certain speech segments were as high as 3 to 5 dB. Perceptually, gains due to variable bit allocation were most noticeable in the 6.4 kb/s system, especially with female speakers. However, even in this case, the benefits of flexible bit allocation were somewhat offset by distortions due to other inadequacies in the coding algorithm. >

There are many studies on age- and gender-dependent analyses in multiple languages. Still, the fundamental frequency (F0) and speech rate in different age and gender groups with Khalkha Mongolian have not yet been studied. In this investigation, we analyzed the speaker’s age and gender effects on F0 and speech rate based on a spontaneous speech corpus of the Khalkha dialect of the Mongolian language. We have discovered some universal aspects of these phonological phenomena and specific relevant characteristics of speech rate change depending on gender. The average speech rate of Khalkha Mongolian is 5.3 syllables per second. The male speakers spoke much quicker than the female speakers. Age and gender affect the F0 and speech rate in particular systematic ways. The F0 of the female and male speakers tended to decrease with age. Also, that elderly male speakers talk slower than elderly female speakers, while young and middle-aged male speakers tend to talk faster than female speakers of the same age.

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.

The accurate recognition of speech is beneficial to the fields of machine translation and intelligent human–computer interaction. After briefly introducing speech recognition algorithms, this study proposed to recognize speech with a recurrent neural network (RNN) and adopted the connectionist temporal classification (CTC) algorithm to align input speech sequences and output text sequences forcibly. Simulation experiments compared the RNN-CTC algorithm with the Gaussian mixture model–hidden Markov model and convolutional neural network-CTC algorithms. The results demonstrated that the more training samples the speech recognition algorithm had, the higher the recognition accuracy of the trained algorithm was, but the training time consumption increased gradually; the more samples a trained speech recognition algorithm had to test, the lower the recognition accuracy and the longer the testing time. The proposed RNN-CTC speech recognition algorithm always had the highest accuracy and the lowest training and testing time among the three algorithms when the number of training and testing samples was the same.

RETRACTED: Real time speech recognition algorithm on embedded system based on continuous Markov model

Speech is a skill that most of the time we take for granted. In reality, this ability is a complex mechanism which requires thoughts to be translated into words who are further transposed into sounds, a mechanism which involves precise coordination of several muscles and joints. In some cases, this complex mechanism can no longer be performed and may be accompanied by almost complete loss of motor activity such in diseases as: stroke, Lock-Down syndrome, amyotrophic lateral sclerosis, cerebral palsy etc. The most recent method that aims to supplement the speech mechanism is imaginary speech recognition using electroencephalographic (EEG) signals, by using complex computing mechanisms like Deep Learning (DL) in order to decode the thoughts. In this paper we aim to recognize three types of clustered phonemes using conventional speech recognition techniques, like Mel-Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC) combined with Convolutional Neural Networks (CNN). We compared four types of features extraction: MFCC, LPC, MFCC+ LPC combined into 1-channel matrix and MFCC+ LPC combined into a 2-channel matrix. We showed that MFCC coefficients offer a better accuracy than LPC and that concatenating MFCC and LPC into a 2-channel matrix we obtain a better performance than combining them into 1-channel matrix.

The aim of the study was to inspect the acoustic properties and sleep characteristics of a preapneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated. Participants with habitual snoring or a heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted, and snoring-related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples, and a machine learning algorithm was used to establish 2 prediction models. A total of 74 eligible participants were included. Model 1, tested by 5-fold cross-validation, achieved an accuracy of 0.92 and an area under the curve of 0.94 for respiratory event prediction. Model 2, with acoustic features and sleep information tested by Leave-One-Out cross-validation, had an accuracy of 0.78 and an area under the curve of 0.80. Sleep position was found to be the most important among all sleep features contributing to the performance of the 2 models. Preapneic sound presented unique acoustic characteristics, and snoring-related breathing sound could be deployed as a real-time apneic event predictor. The models, combined with sleep information, serve as a promising tool for an early warning system to forecast apneic events. Wang B, Yi X, Gao J, etal. Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal. J Clin Sleep Med. 2021;17(9):1777-1784.

In Malaysia, most people speak several varieties of English known as Malaysian English (MalE) and there is no uniform version because of the existence of multi-ethnic population. It is a common scenario that Malaysians speak a particular local Malay, Chinese or Indian English accent. As most commercial speech recognizers have been developed using a standard English language, it is a challenging task for achieving highly efficient performance when other accented speech are presented to this system. Accent identification (AccID) can be one of the subsystem in speaker independent automatic speech recognition (SI-ASR) system so that deterioration issue in its performance can be tackled. In this paper, the most important speech features of three ethnic groups of MalE speakers are extracted using Linear Predictive Coding (LPC), formant and log energy feature vectors. In the subsequent stage, the accent identity of a speaker is predicted using K-Nearest Neighbors (KNN) classifier based on the extracted information. Prior, the preprocessing parameters and LPC order are investigated to properly extract the speech features. This study is conducted on a small set speech corpus developed as pilot study to determine the feasibility of automatic AccID of MalE speakers which has never been reported before. The experimental results indicate a highly promising recognition accuracy of 94.2% upon feature fusion sets of LPC, formants and log energy.