Speech Endpoint Detection Research Articles

Speech endpoint detection method based on logarithmic energy entropy product of adaptive sub-bands in low signal-to-noise ratio environments

Speech endpoint detection method based on logarithmic energy entropy product of adaptive sub-bands in low signal-to-noise ratio environments

Speech endpoint detection method based on logarithmic energy entropy product of adaptive sub-bands in low signal-to-noise ratio environments

Speech endpoint detection method based on logarithmic energy entropy product of adaptive sub-bands in low signal-to-noise ratio environments

Construction of an innovative model of English online teaching based on a multimedia English learning system model

Abstract This paper explores a multimedia learning system suitable for students’ independent English learning through the current development of assisted English learning systems, university-level English teaching requirements, and learner characteristics. Based on the model of the proposed multimedia English learning system, in the endpoint detection stage of speech, short-time excess rate and short-time energy are combined to process the endpoint detection of speech. In the final speech comparison stage, the dynamic time planning algorithm DTW is analyzed and improved before the similarity recognition of speech. We also complete the requirement analysis of the English-speaking learning system by combining the relevant theories and applying the speech signal processing technology and template matching technology to realize a multimedia-assisted learning system suitable for college students to conduct independent English learning. Through the English-speaking test of two classes, the control class using traditional online teaching had many failures reaching 25%, while the experimental class using the English-assisted learning platform passed all of them. 80 points or more were achieved by only 5% of the control class, while 20% of the experimental class. The average score of the control class was 63.4, while the experimental class was 72.1, and the experimental class was 8.7 points higher than the control class. Therefore, teaching English-assisted learning platforms can improve students’ English-speaking abilities.

Intelligent Speech Garbage Classification Based on Deep Residual Network

To solve the problem that there are many kinds of municipal solid waste and urban residents do not have a clear classification standard for garbage, an intelligent voice garbage classification system based on a deep residual neural network is designed. For the four kinds of common garbage in life, the voice commands of four kinds of typical garbage are collected. Through the speech endpoint detection algorithm, the silence and noise in the speech are removed, and the acoustic model based on the deep residual network is built to realize garbage classification. The experimental results show that after the model training is completed, the accuracy of the verification set reaches 98%, the accuracy of the test set reaches 96%, and the generalization ability is strong, which has a high application value.

Integrated Design of Financial Self-Service Terminal Based on Artificial Intelligence Voice Interaction.

Integrated design of financial self-service terminal based on artificial intelligence voice interaction with the rapid development of science and technology, artificial intelligence technology is deepening in the field of intelligence and automation. The financial industry is the lifeblood of a country’s economy, with great growth potential and high growth rate. The integrated design of intelligent financial self-service terminal has become an important topic in the field of rapid development of social economy and science and technology. Therefore, this paper designs the integration of financial self-service terminal based on artificial intelligence voice interaction. First, this paper introduces the meaning and composition of financial self-service terminal integration, then studies the voice interaction principle based on artificial intelligence technology, and designs the integrated structure of financial self-service terminal with voice interaction. After that, this paper makes a series of tests on voice interaction technology, user experience, and the performance of financial self-service terminal. Finally, the test results of voice interaction are as follows: the delay estimation results of voice interaction of the terminal are relatively accurate, and the error points are basically within five sampling points, which indicate that the delay estimation algorithm is practical. The endpoint detection method based on CO complexity can effectively overcome the impact of noise environment on speech endpoint detection system and is suitable for the requirements of robust speech recognition system. Considering that the actual application scenario of voice positioning can judge the speaker’s position and turn to the speaker’s direction during human–computer interaction, the azimuth error is acceptable within a few degrees to meet the application requirements. The direction angle error is acceptable within a few degrees to meet the application requirements. The accuracy of the improved algorithm is improved in intercepting effective speech signals. The terminal has short running time and delay time, small memory, and central processing unit (CPU) occupation and can meet the needs of users. The speech recognition accuracy of the financial self-service terminal basically reaches more than 80%, which can basically meet the daily needs.

Digital Music Feature Recognition Based on Wireless Sensing Technology

With the rapid development of information technology, digital music is subsequently increasing in large quantities, and how a good integration of vocal input and recognition technology can be transformed into digital music can greatly improve the efficiency of music production while ensuring the quality and effect of music. This paper focuses on the implementation and application of human voice input and recognition technology in digital music creation, enabling users to generate digital music forms by simply humming a melodic fragment of a piece of music into a microphone. The paper begins with an introduction to digital music and speech recognition technology and goes on to describe the respective characteristics of various audio formats, which are selected as data sources for digital music creation based on the advantages of the files in terms of retrieval. Following that, the method of extracting musical information from music is described, and the main melody is successfully extracted from the multitrack file to extract the corresponding musical performance information. The feature extraction of humming input melody is further described in detail. The traditional speech recognition method of using short-time energy and short-time overzero rate features for speech endpoint detection is analyzed. Combining the characteristics of humming music, the method of cutting notes by two-stage cutting mode, i.e., combining energy saliency index, overzero rate, and pitch change, is adopted to cut notes, which leads to a substantial improvement in performance. The algorithm uses the melody extraction algorithm to obtain the melody line, merges the short-time segments of the melody line to reduce the error rate of emotion recognition, uses the melody line to segment the music signal to generate segmented segments, then abstracts the features of the segmented segments through a CNN-based structural model, and inputs the output of the model to the regressor in cascade with the melody contour features of the corresponding segmented segments to finally obtain the emotion V / A value of the segmented segments.

Speech endpoint detection in fixed differential beamforming combined with modulation domain

The actual speech signal is always in a complex environment, which affects the accuracy of speech endpoint detection. In order to improve the accuracy of speech endpoint detection under low Signal-To-Noise Ratio(SNR), a speech endpoint detection technology based on fixed differential beamforming and modulation domain is proposed. Firstly, two microphones of the first-order differential microphone arrays(DMAs) are used to collect the target speech and noise, and then a fixed differential beamformer is used to reduce the noise. Then, the phase compensated modulation domain spectral subtraction method is used to further eliminate the residual noise. Finally, the subband spectral entropy algorithm is used to detect the speech signal endpoint. The experimental results show that, compared with the existing methods, the proposed method has a higher accuracy of speech endpoint detection in the low SNR environment of -10∼5dB.

Research Status and Future Development of Endpoint Detection Algorithms Based on Computer Science Language Signals

Today is an information technology era, language signal recognition technology, language signal coding technology and a variety of new language technology will be widely used in all areas of our life, such as: security field, human-computer interaction, communication field. We can accurately analyze pure speech signals and silent segments in a speech by using language signal endpoint detection technology, which will bring decisive effects to the efficiency of ASR and ASC. Three steps can be used to represent the language endpoint detection model, namely, the language signal preprocessing step, the extraction of the characteristic vector of the whole language flow, and the establishment of the language endpoint discrimination model. Finally, the speech endpoint discrimination model is established. The traditional speech endpoint detection algorithms include the two threshold method based on time domain, the universal entropy method based on frequency domain and the invert characteristic method. Aiming at the low SNR and complex noise environment, in order to obtain satisfactory endpoint detection effect, this paper proposes an endpoint detection model based on optimized Extreme Learning machine (ELM), and makes up for the deficiencies of the algorithm itself by optimizing network connection parameters[1-2].

Development of Wireless Sensor Device for Machine English Oral Pronunciation Noise Detection

There is often noise in spoken machine English, which affects the accuracy of pronunciation. Therefore, how to accurately detect the noise in machine English spoken language and give standard spoken pronunciation is very important and meaningful. The traditional machine‐oriented spoken English speech noise detection technology is limited to the improvement of software algorithm, mainly including speech enhancement technology and speech endpoint detection technology. Based on this, this paper will develop a wireless sensor network based on machine English oral pronunciation noise based on air and nonair conduction, reasonably design and configure air sensors, and nonair conduction sensors to deal with machine English oral pronunciation noise, so as to improve the naturalness and intelligibility of machine English speech. At the hardware level, this paper mainly optimizes the AD sampling, sensor matching layout, and internal hardware circuit board layout of the two types of sensors, so as to solve the compatibility problem between them and further reduce the hardware power consumption. In order to further verify or evaluate the performance of the machine spoken English speech noise detection sensor designed in this paper, a machine spoken English training system based on Android platform is designed. Compared with the traditional system, the training system can improve the intelligence of machine oriented oral English noise detection algorithm, so as to continuously improve the accuracy of system detection. The machine English pronunciation is adjusted and corrected by combining the data sensed by the sensor, so as to form a closed‐loop design. The experimental results show that the wireless sensor sample proposed in this paper has obvious advantages in detecting the accuracy of machine English oral pronunciation, and its good closed‐loop system is helpful to further improve the accuracy of machine English oral pronunciation.

An Improved Speech Segmentation and Clustering Algorithm Based on SOM and K-Means

This paper studies the segmentation and clustering of speaker speech. In order to improve the accuracy of speech endpoint detection, the traditional double-threshold short-time average zero-crossing rate is replaced by a better spectrum centroid feature, and the local maxima of the statistical feature sequence histogram are used to select the threshold, and a new speech endpoint detection algorithm is proposed. Compared with the traditional double-threshold algorithm, it effectively improves the detection accuracy and antinoise in low SNR. The k-means algorithm of conventional clustering needs to give the number of clusters in advance and is greatly affected by the choice of initial cluster centers. At the same time, the self-organizing neural network algorithm converges slowly and cannot provide accurate clustering information. An improved k-means speaker clustering algorithm based on self-organizing neural network is proposed. The number of clusters is predicted by the winning situation of the competitive neurons in the trained network, and the weights of the neurons are used as the initial cluster centers of the k-means algorithm. The experimental results of multiperson mixed speech segmentation show that the proposed algorithm can effectively improve the accuracy of speech clustering and make up for the shortcomings of the k-means algorithm and self-organizing neural network algorithm.

Savitzky-Golay Filtering and Improved Energy Entropy for Speech Endpoint Detection under Low SNR

Under the condition of low signal-to-noise ratio (SNR) or non-stationary noise, the performance of endpoint detection is poor. Therefore, this paper proposes a speech endpoint detection algorithm for low SNR. In this paper, Savitzky-Golay filtering, improved sub-band energy entropy and constant Q-transform (CQT) are used to extract features, and single parameter double threshold method is used to realize endpoint detection. In this paper, clean speech and noise-92 speech fragments are used to evaluate the algorithm. Experimental results show that the algorithm in this paper can distinguish speech endpoints well. For Gaussian white noise, Factory noise and Volvo noise, the detection accuracy of the improved algorithm is improved by 5.5%, 5.3% and 4.6% respectively. Therefore, the algorithm in this paper can separate the mute and voice in complex environment.

Speech Endpoint Detection Based on Fractal Dimension with Adaptive Threshold

Speech Endpoint Detection Based on Fractal Dimension with Adaptive Threshold

End-to-End Speech Endpoint Detection Utilizing Acoustic and Language Modeling Knowledge for Online Low-Latency Speech Recognition

Speech endpoint detection (EPD) benefits from the decoder state features (DSFs) of online automatic speech recognition (ASR) system. However, the DSFs are obtained via the ASR decoding process, which can become prohibitively expensive especially in limited-resource scenarios such as the embedded devices. To address this problem, this paper proposes a language model (LM)-based end-of-utterance (EOU) predictor, which is trained to determine the framewise probabilities of the EOU token conditioned on the previous word history obtained from the 1-best decoding hypothesis of the ASR system in an end-to-end manner without an actual decoding process in the test step. Further, a novel end-to-end EPD strategy is presented to incorporate a phonetic embedding (PE)-based acoustic modeling knowledge and the proposed EOU predictor-based language modeling knowledge into an acoustic feature embedding (AFE)-based EPD approach within the recurrent neural networks (RNN)-based EPD framework. The proposed EPD algorithm is built upon the ensemble RNNs, which are independently trained for the three parts, which are the proposed LM-based EOU predictor, AFE-based EPD, and PE-based acoustic model (AM) in accordance with each target. The ensemble RNNs are concatenated at the level of the last hidden layers and then attached into the fully-connected deep neural networks (DNN)-based EPD classifier, which is trained in accordance with the ultimate EPD target. Thereafter, they are jointly retrained at the second step of the DNN training to yield the lower endpoint error. The proposed EPD framework was evaluated in terms of the endpoint accuracy and word error rate for the CHiME-3 and large-scale ASR tasks. The experimental results turn out that the proposed EPD algorithm efficiently outperforms the conventional EPD approaches.

Health status monitoring system based on speech analysis

Abstract In recent years, the health of the elderly has become the focus of medical treatment. In daily life, the physical condition of the elderly cannot be fed back to their families in time, and the health level of the elderly at home cannot be accurately judged. In order to solve the above problems, we put forward a method to monitor the health of the elderly with speech recognition system. This method can not only monitor the health of the elderly in real time, but also inform the family members in time when there is something wrong with their health. The specific steps are as follows: firstly, sound waves are converted into electrical signals by sound sensors. Because the frequency band distribution of the input sound wave signals is complex, we choose band-pass filters to shield other noises, and choose the sound frequency band belonging to specific elderly people as input signals. Because of the particularity of sound wave signals, they are time-varying signals, which need to be processed. We extract characteristic parameters and frame-by-frame stable digital signals to obtain stable signals for subsequent processing. The filter and signal processing chip are designed by FPGA, and the finished product is prepared after layout drawing. In the process of protecting the health of the elderly, the invention is equipped with image recognition, which is convenient for timely monitoring the living state of the elderly and whether there is sudden disease. Comprehensive speech recognition chip is used to protect the healthy life of the elderly. template matching Speech endpoint detection Speech database , Feature parameter extraction - - Template matching Judging whether the elderly are healthy or not Computational recognition Figure -Speech in training set (verification) Test set - : la oc Outside the training set Speech with noisy Voice (test) background Unrecognized speech Figure2

An overview of speech endpoint detection algorithms

Speech endpoint detection is an important part of modern speech information processing technology. The success of endpoint detection directly improves the performance and quality of speech coding, speech recognition, speech synthesis and human interaction. The robustness and detection accuracy of algorithms have always been hot topics for many scholars in the condition of low Signal-to-Noise Ratio (SNR) and complex noise. In this paper, we aim to provide an overview of the state-of-the-art in time domain, frequency domain and cepstrum domain for speech endpoint detection algorithms and to cast a glance at the challenges for future research.

Research on Snoring Recognition Algorithms

A snoring recognition algorithm based on machine learning is proposed to effectively and precisely recognize snoring. To obtain a dataset, the speech endpoint detection algorithm and Mel frequency cepstrum coefficient feature extraction algorithm are applied to process speech signal samples. The dataset is classified into snoring and nonsnoring data (other speech signals) using support vector machines. Experimental results show that the algorithm recognizes snoring signals with a high accuracy rate of 97% and positively impacts subsequent research and related engineering applications.

Precise detection of speech endpoints dynamically: A wavelet convolution based approach

Precise detection of speech endpoints is an important factor which affects the performance of the systems where speech utterances need to be extracted from the speech signal such as Automatic Speech Recognition (ASR) system. Existing endpoint detection (EPD) methods mostly uses Short-Term Energy (STE), Zero-Crossing Rate (ZCR) based approaches and their variants. But STE and ZCR based EPD algorithms often fail in the presence of Non-speech Sound Artifacts (NSAs) produced by the speakers. Pattern recognition and classification techniques are also applied but those methods require labeled data for training. In this article, a novel approach is proposed to extract speech endpoints and the algorithm is termed as Wavelet Convolution based Speech Endpoint Detection (WCSED). WCSED decomposes the speech signal into high-frequency and low-frequency components using wavelet convolution and then computes information-entropy based thresholds for the two frequency components. The low-frequency thresholds are used to extract voiced speech segments, whereas the high-frequency thresholds are used to extract the unvoiced speech segments by filtering out the NSAs. WCSED does not require any labeled data for training and can automatically extract speech segments. Experiments are carried out on two speech databases and the results are promising even in the presence of NSAs.

LTSD and GDMD features for Telephone Speech Endpoint Detection

AbstractThis paper proposes a new contour-based speech endpoint detector which combines the log-Group Delay Mean-Delta (log-GDMD) feature, an adaptive twothreshold scheme and an eight-state automaton. The adaptive thresholds scheme uses two pairs of thresholds - for the starting and for the ending points, respectively. Each pair of thresholds is calculated by using the contour characteristics in the corresponded region of the utterance. The experimental results have shown that the proposed detector demonstrates better performance compared to the Long-Term Spectral Divergence (LTSD) one in terms of endpoint accuracy. Additional fixed-text speaker verification tests with short phrases of telephone speech based on the Dynamic Time Warping (DTW) and left-to-right Hidden Markov Model (HMM) frameworks confirm the improvements of the verification rate due to the better endpoint accuracy.

An adaptive speech endpoint detection method in low SNR environments

Endpoint detection of speech has been shown prosperous for speech recognition and speech enhancement. But the traditional endpoint detection methods lose efficiency in either low signal-to-noise ratio (SNR) environments or nonstationary noise environments. To improve the accuracy of speech endpoint detection in low SNR environments, an endpoint detection method based on an adaptive algorithm for thresholds adjustment is put forward in this paper. The spectral subtraction of multitaper spectrum estimation is performed to enhance the speech. During the process of detection, the cepstral distance of Mel frequency cepstrum coefficient (MFCC) is utilized and the thresholds are adaptively adjusted to different environments. Simulation experiments indicate that in different noise environments with different SNRs, our algorithm has a better endpoint detection accuracy compared with other detection algorithms. Besides that, the algorithm also exhibits strong robustness in low SNR environments.

Research on Speech Endpoint Detection Algorithm with Low SNR

The detection of speech endpoint is an important application for speech signal processing. Although there are variance methods, the endpoint can’t be detected accurately in low SNR (Signal to Noise Ratio). The paper pointes out an endpoint detection algorithm combining two methods together: the one is improved spectral subtraction based on multitaper spectral estimation, and the other is BARK subband variance in frequency domain. Firstly, the noisy speech signal is processed though the improved spectral subtraction based on multitaper spectral estimation. It can achieve the purpose of noise reduction through this step. Then the noisy speech signal is detected using the method of BARK subband variance in frequency domain. Compared with the common endpoint detection algorithm, it is concluded that endpoint detection accuracy by new method can be improved in low SNR.