Speech Signal Acquisition Research Articles

Application of Big Data-based Information Technology in Business English Courses in Colleges and Universities

Abstract In this paper, after using Cool Edit Pro2.1 software to complete the speech signal acquisition work, the DTW method is used to pre-emphasize the speech signal. Due to the small continuous correlation between adjacent frames, the use of windowing and frame-splitting operation makes the amplitude change of the speech signal smooth and obtains the spectral envelope of the speech signal. The negative impact of MFCC feature parameter extraction is optimized by parameterizing the volume intensity and fundamental trajectory, and the application of the English scoring mechanism in business English education is explored. According to the current situation of business English teaching in colleges and universities, research problems and sampling objects are identified, and statistical analysis is applied to analyze the teaching effect of business English informatization by example. The results show that the speech evaluation system of spoken business English based on DTW does not differ much from the similar results of the scoring of the master’s degree of English majors in our university, and its difference value always stays between 1 and 10. In terms of the simple correlation coefficient and significance level (0.01), the simple correlation coefficient between teaching effectiveness and the use of technological media is 0.385 (P=0.00), which means that there is a significant correlation between both teaching effectiveness and the use of technological media. This study improves the standardization of English pronunciation among students, which is important for the development of business English in colleges and universities.

Design of fully steerable broadband beamformers with concentric circular superarrays.

Concentric circular microphone arrays have been used in a wide range of applications, such as teleconferencing systems and smarthome devices for speech signal acquisition. Such arrays are generally designed with omnidirectional sensors, and the associated beamformers are fully steerable but only in the sensors' plane. If operated in the three-dimensional space, the performance of those arrays would suffer from significant degradation if the sound sources are out of the sensors' plane, which happens due to the incomplete spatial sampling of the sound field. This paper addresses this issue by presenting a new method to design concentric circular microphone arrays using both omnidirectional microphones and bidirectional microphones (directional sensors with dipole-shaped patterns). Such arrays are referred to as superarrays as they are able to achieve higher array gain as compared to their traditional counterparts with omnidirectional sensors. It is shown that, with the use of bidirectional microphones, the spatial harmonic components that are missing in the traditional arrays are compensated back. A beamforming method is then presented to design beamformers that can achieve frequency-invariant beampatterns with high directivity and are fully steerable in the three-dimensional space. Simulations and real experiments validate the effectiveness and good properties of the presented method.

Design of 2D and 3D Differential Microphone Arrays With a Multistage Framework

Differential microphone arrays (DMAs) have demonstrated a great potential for high-fidelity acoustic and speech signal acquisition in a wide range of applications since such arrays are able to achieve frequency-invariant beampatterns with high directivity. Consequently, a great number of efforts have been devoted to the design of DMAs and the associated beamformers in the literature. However, most of the methods only work for arrays with particular topologies, e.g., linear, circular, concentric circular, and spherical ones. How to design general two-dimensional (2D) and three-dimensional (3D) DMAs that can measure the desired differential sound field and form the desired spatial response in the 3D space remains an unsolved problem. This paper investigates this problem and presents a multistage design approach. The major contributions of this work are as follows. First, we reexamine the differentials of the acoustic pressure field in the 3D space and derive the general expression of the directivity patterns resulting from the spatial differential operation, which serves as the foundation for differential beamforming with 2D or 3D microphone arrays. Second, we present a multistage approach to the design of 2D and 3D DMAs, and deduce the relationship between the global beamformer and the beamformers at different stages as well as the relationship between their beampatterns. Third, several algorithms are presented for the design of differential as well as robust differential beamformers in this multistage framework. Simulation results validate the proposed approach and justifies its properties.

A New Method to Design Steerable First-Order Differential Beamformers

First-order differential microphone arrays (FODMAs), which combine a small-spacing uniform linear array and a first-order differential beamformer, have been used in a wide range of applications for sound and speech signal acquisition. However, traditional FODMAs are not steerable and their main lobe can only be at the endfire directions. To circumvent this problem, we propose in this letter a new method to design steerable FODMAs. We first divide the target beampattern into a sum of two sub-beampatterns, i.e., cardioid and dipole, where the summation is controlled by the steering angle. We then design two sub-beamformers, one is similar to the traditional approach and is used to achieve the cardioid sub-beampattern, while the other is designed to filter the squared observation signals and is used to approximate the dipole sub-beampattern. The overall beampattern resembles the target beampattern for any steering angle. Simulations and experiments are performed to justify the effectiveness of the developed method.

Heterophasic Binaural Differential Beamforming for Speech Intelligibility Improvement

Differential beamformers with small-size microphone arrays are very attractive for audio and speech signal acquisition thanks to their high directivity and frequency-invariant spatial responses. However, such beamformers often suffer from significant white noise amplification at low frequencies, which makes their implementation in real-world systems challenging. One widely used way to circumvent this issue is to increase the number of microphones in the design of a given order differential beamformer, leading to the so-called robust differential beamformer in which the redundancy provided by the additional sensors are used to improve the white noise gain (WNG). But even with this robust solution, white noise amplification at low frequencies still exists. In this article, instead of trying to improve WNG, we adopt a method to render the white noise in such a way that it affects less the perception of the speech signal of interest. Specifically, we propose a binaural differential beamforming method in which a differential beamformer is designed with two sub-beamforming filters that simultaneously generates two outputs, one for the left ear and the other for the right ear. Motivated by psychoacoustic experiments, we design these two filters in such a way that they are orthogonal so that the coherence between the white noise at the binaural outputs is minimized while the coherence between diffuse noise is maximized. With the proposed binaural differential beamformers, the desired signal components and (amplified) white noise at the binaural differential beamformer's outputs are rendered into different directions or zones. Consequently, the human perception system can better distinguish the desired signal from white noise for improved speech intelligibility. The superiority of the proposed binaural beamforming technique is justified by simulations, experiments, as well as listening tests.

Continuously steerable differential beamformers with null constraints for circular microphone arrays.

Differential beamforming combined with microphone arrays can be used in a wide range of applications related to acoustic and speech signal acquisition and recovery. A practical and useful method for designing differential beamformers is the so-called null-constrained method, which was developed based on linear arrays and requires only the nulls' information from the target directivity pattern. While it is effective and easy to use, this method is found not suitable for designing steerable differential beamformers with circular arrays. This paper reexamines this technique in the context of circular differential microphone arrays. By analyzing the properties of the circular array topology, the null-constrained method is extended to include symmetric constraints, which is inherent in the design of circular arrays. This extension yields a design method for fully steerable differential beamformers that require only minimum information from the target beampattern. Simulations justify the theoretical analysis and demonstrate the good properties of the developed method.

Multidimensional feature diversity based speech signal acquisition

Computer intelligence methods are the buzz of identifying the state and context of diversified domains. The computer intelligence methods mostly fall into the domain of machine learning, which often trained by the known data of the domain context and state. These methods even spanned significantly to the diversified contexts of speech domains such as speech recognition, emotion recognition, speaker recognition, and many more. However, the contemporary methods of speech acquisition or speech separation have majorly relied on filters and other signal processing techniques, which are considerably underrated to identify the speech signal that exists among the highly correlated noise signals. In this context, the contribution of this manuscript is a machine learning mechanism that intended to identify the speech signal bounded with highly correlated noise signals. The method proposed is using the diversified features extracted from the signals representing the speech and other correlated noises. The experimental study has carried on the signal and correlated noise utterances collected from the benchmark dataset CHiME-5, which has meant for machine learning-based signal processing. The performance analysis of the proposed model has scaled by comparing it with the contemporary model.

Multichannel Speech Acquisition and Analysis for Computer-Aided Sigmatism Diagnosis in Children

A novel concept for acoustic data acquisition for computer-aided diagnosis of a common speech disorder (sigmatism) is presented in this paper. We designed and built a data acquisition device enabling repeatable speech signal acquisition in up to fifteen spatially-organized acoustic channels. The system is safe, non-invasive, comfortable, visually attractive to the user, and does not affect the articulation process. It is easy and convenient to use and transport and does not require a specialized measuring room. We collected a large speech corpus containing speech samples from 107 children aged five or six. The data were acquired according to a dedicated protocol. They consisted of multichannel acoustic recordings of selected words containing sibilants and diagnostic descriptions of articulatory features prepared by speech therapy experts. The data acquisition device was examined for responses and repeatability of individual microphones in the presence of various synthetic and human-generated acoustic stimuli. Then, it was verified for its ability to indicate distinctive patterns in spatial energy distribution in different realizations of two sibilants: /s/, /∫/ in three pronunciation categories each, based on collected speech-articulation corpus. The results confirm that a multichannel speech signal can be successfully employed for the analysis of the spatial distribution of airflow during normative or pathological realization of sibilant sounds in children. The method is promising for comprehensive analysis of articulatory features, which follows new trends in the description of speech disorders; such an approach was not employed in speech diagnosis or therapy so far.

Realization of Spectrum Analysis of Speech Signal Based

MATLAB is a very powerful engineering and practical software for data analysis and processing. It is very convenient to analyze and deal with it. This paper introduces how to collect the speech signal and voice signal acquisition in MATLAB environment, and analyze the MATLAB of speech signal processing by an example. The basic principle and basic process of texture image analysis based on Fourier transform spectrum are described by MATLAB simulation of sinusoidal texture image and spectral characteristic analysis of sinusoidal texture image. The program is written, and the spatial texture image with ideal sine distribution is obtained, and its spectral characteristics are analyzed. According to the defi nition of discrete Fourier transform DFT, a method of calculating the amplitude spectrum of discrete signal by DFT is deduced. The eff ectiveness of the method is verifi ed by MATLAB simulation.

Implementation of a Hearing Aid using Texas TMS320C6713 Digital Signal Processor

Objectives: In this paper, design methods of software and hardware are introduced forreal time speech signal acquisition and processing based on DSP chip TMS320C6713 to eliminate acoustic noise. Methods: Real time Digital Signal Processors (DSP) are replacing the microcontrollersin the embedded industry for multiple applications. A low pass FIR filter is designed both on this processor and on Simulink to compare the performance. The powerful Texas TMS320C6713 kit makes signal processing much simpler and robust. Findings: Acoustic noise is the primary reason in degrading speech perception while using hearing aids. In real time mode the removal of noise in the speech is comparatively difficult. The experimental results show that after filtering the real time speech signal through the designed filter, there is a considerable improvement in the real-time speech signal where the SNR has improved. Application: With the scaling down of chip technology in terms of both size and cost this method can be easily incorporated in tiny hearing aids.

Reduced-Order Robust Superdirective Beamforming With Uniform Linear Microphone Arrays

Sensor arrays for audio and speech signal acquisition are generally required to have frequency-invariant beampatterns to avoid adding spectral distortion to the broadband signals of interest. One way to obtain frequency-invariant beampatterns is via superdirective beamforming. However, traditional superdirective beamformers may cause significant white noise amplification (particularly at low frequencies), making them sensitive to uncorrelated white noise. To circumvent the problem of white noise amplification, a method was developed to find the superdirective beamforming filter with a constraint on the white noise gain (WNG), leading to the so-called WNG-constrained superdirective beamformer. But this method damages the frequency invariance of the beampattern. In this paper, we develop a flatness-constrained robust superdirective beamformer. We divide the overall beamformer into two subbeamformers, which are convolved together: one subbeamformer forms a lower order superdirective beampattern while the other attempts to improve the WNG. We show that this robust approach can improve the WNG while limiting the frequency dependency of the beampattern at the same time.

Research on Acquisition and Process of Speech Signals

Research on Acquisition and Process of Speech Signals

Design of Voice Signal Visualization Acquisition System Based on Sound Card and MATLAB

With the development of computer technology and information technology, voice interaction has become a necessary means of human-computer interaction, and voice signal acquisition and processing is the precondition and foundation of human-computer interaction. This paper introduces the MATLAB visualization method into voice signal acquisition system, and uses MATLAB programming method to drive sound card directly, which realizes the identification and acquisition of voice signal and designs a new voice signal visualization acquisition system. In order to optimize the system, this paper introduces the variance analysis algorithm into the design of visualization system, which realizes the optimization of voice signal recognition model with different level parameters. At the end this paper does numerical simulation on the speech signal acquisition system; through signal acquisition 2D and 3D visualization voice signals are obtained. It extracts single signal characteristics, which provides a theoretical reference for the design of signal acquisition system.

Implementation and Design of Underwater Duplex Speech Communication System Based on Vector Hydrophone

This paper realizes 1.2kbps Mixed Excitation Linear Prediction (MELP) speech compression algorithm through multi-frame joint and Vector Quantization technology. In order to adapt to underwater acoustic channel, we apply the Orthogonal Frequency Division Modulation (OFDM) to modulate the source bits, together with synchronization, channel estimation and RS error correcting code technology. The hardware platform of this system relies on TMS320DM642 and AIC23. Moreover, we introduce single vector hydrophone to restrain the local source. The system finished duplex speech signal acquisition and transmission and tank test results prove that the synthesized speech is clear and understandable and it has a stable performance.

Design of Speech Signal Acquisition and Processing System

The design of a speech signal acquisition and processing system is introduced. The collected data is analyzed and processed Using the powerful Matlab software, and this system is verified through the example.The system of mobile communication provides theoretical reference value in high quality speech communication service .

Speech Signal Acquisition and Processing Based on DSP

The design method of software and hardware was introduced about speech signal acquisition and process based on DSP chip TMS320C64X produced by TI company in this paper. The principle figure of the system, hardware interface circuits, software flow chart as well as relational experiment data and waves were also present.

Combined Handwriting and Speech Modalities for User Authentication

In this paper, we report on the development of an efficient user authentication system based on a combined acquisition of online pen and speech signals. The novelty of our approach is in the simultaneous recording of these two modalities, simply asking the user to utter what she/he is writing. The main benefit of this multimodal approach is a better accuracy at no extra costs in terms of access time or inconvenience. Another benefit comes from an increased difficulty for forgers willing to perform imitation attacks as two signals need to be reproduced. We are comparing here two potential scenarios of use. The first one is called <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spoken</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">signatures</i> where the user signs and says the content of the signature. The second scenario is based on <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spoken</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">handwriting</i> where the user is prompted to write and read the content of sentences randomly extracted from a text. Data according to these two scenarios have been recorded from a set of 70 users. In the first part of this paper, we describe the acquisition procedure, and we comment on the viability and usability of such simultaneous recordings. Our conclusions are supported by a short survey performed with the users. In the second part, we present the authentication systems that we have developed for both scenarios. More specifically, our strategy was to model independently both streams of data and to perform a fusion at the score level. Starting from a state-of-the-art-modeling algorithm based on Gaussian Mixture Models trained with an Expectation-Maximization procedure, we report on several significant improvements that are brought. As a general observation, the use of both modalities outperforms significantly the modalities used alone.

Multichannel Bin-Wise Robust Frequency-Domain Adaptive Filtering and Its Application to Adaptive Beamforming

Least-squares error (LSE) or mean-squared error (MSE) optimization criteria lead to adaptive filters that are highly sensitive to impulsive noise. The sensitivity to noise bursts increases with the convergence speed of the adaptation algorithm and limits the performance of signal processing algorithms, especially when fast convergence is required, as for example, in adaptive beamforming for speech and audio signal acquisition or acoustic echo cancellation. In these applications, noise bursts are frequently due to undetected double-talk. In this paper, we present impulsive noise robust multichannel frequency-domain adaptive filters (MC-FDAFs) based on outlier-robust M-estimation using a Newton algorithm and a discrete Newton algorithm, which are especially designed for frequency bin-wise adaptation control. Bin-wise adaptation and control in the frequency-domain enables the application of the outlier-robust MC-FDAFs to a generalized sidelobe canceler (GSC) using an adaptive blocking matrix for speech and audio signal acquisition. It is shown that the improved robustness leads to faster convergence and to higher interference suppression relative to nonrobust adaptation algorithms, especially during periods of strong interference

Self-calibrating microphone arrays for speech signal acquisition: A systematic approach

The application of microphone arrays and beamforming techniques for speech acquisition promises significant improvement compared to systems operating with a single microphone. Adaptive beamformers offer a potentially superior performance to fixed beamformers particularly in the case of time varying sound field characteristics or in the case of coherent noise such as interfering speakers, loudspeaker signals, etc. However, for real-world applications adaptive beamformers hold the risk of severe signal degradation. Disturbances such as mismatched microphones, an imprecise steering direction or reverberation due to multi-path propagation may cause an adaptive beamformer to distort the desired signal. Microphone mismatch naturally arises from production tolerances as well as from aging effects in the long run. This contribution presents a class of adaptive self-calibration methods. These methods perform a calibration in the background during normal operation of the system and therefore save the need for an additional costly calibration procedure. Based on a systematic approach, new configurations as well as some well-known configurations are derived. The performance of the different self-calibration configurations is examined in a car environment.

Introduction to the Special Issue on Multichannel Signal Processing for Audio and Acoustics Applications

HE IEEE Signal Processing Society has its roots in an area where acoustics, speech, and signal processing converge, as was reflected in the former name of the society when it was founded in 1974. The interface between acoustics, speech, and signal processing is still an area of great interest to the society, with many fundamental problems still unsolved. Research is driven by applications where acoustic signals have to be captured, transmitted, and/or reproduced in an acoustic environment that includes echoes, noise, and reverberation Considering human/machine interfaces as a major area of applications, it is obvious that signal processing becomes more challenging as the distance between humans and the machines increases, as the signal bandwidth increases, and as the acoustic environment becomes more complex and hostile. Increasingly sophisticated algorithms have been developed since the mid-1970s and along with the availability of greatly increased and affordable computational power, multichannel signal processing algorithms naturally evolved for exploiting the spatial dimension of acoustic signals. The importance and popularity of this field was well reflected by the large number of submissions to this special issue. The volume of high-quality papers could not be fitted into the page budget allotted to us. Thus, we regrettably had to decide to publish some of them in a second section of this special issue as part of a regular issue of the TRANSACTIONS in early 2005. For sound reproduction, where we want to provide a pair of desired signals at the listeners’ ear drums, seamless human/machine interfaces based on multichannel techniques have been implemented since the invention of stereo systems. However, providing the true spatial sound experience in large listening spaces became possible only with new multichannel signal processing techniques, such as wavefield synthesis. Still, major challenges remain, especially phase-true equalization of listening room acoustics and the cancellation of local noise sources and interferers. On the other hand, acquisition of audio and speech signals has been a research topic since the invention of the microphone and still today presents major challenges for the signal processing community. Structurally the simplest problem, the acoustic feedback from loudspeakers into microphones is addressed by acoustic echo cancellation: From the single-channel case which has been investigated since the 1970s, research has moved on to stereo and multichannel reproduction, recently culminating in a new wave-domain adaptive filtering concept which has been presented for the first time at ICASSP 2004. For removing unwanted interference and noise from desired signals, multichannel techniques utilize spatial diversity to discriminate between desired and undesired components, either by exploiting different spatial coherence properties or by beamforming, which directs a beam of increased sensitivity towards the desired source. For traditional beamforming, source localization is necessary if the location of the source is not known a priori. Reflecting current research emphasis in these areas, the paper by Cohen addresses filtering of the