Speech Signal Processing System Research Articles

Application of deep learning-based speech signal processing technology in electronic communication

In recent years, the artificial intelligence boom triggered by deep learning is influencing and changing peoples lifestyles. People are no longer satisfied with human-computer interaction through simple text commands; instead, they look forward to more convenient and faster communication methods like voice interaction. Against the backdrop of innovative development, the application of speech signal processing systems is becoming increasingly widespread. Therefore, it is necessary to study the application of deep learning-based speech signal processing technology in electronic communication. This can provide more valuable references and assistance for future development, promoting the better development of deep learning-based speech signal processing technology in electronic communication. In this paper, we first review the application of deep learning in speech signal enhancement, speech recognition, and speech synthesis from a theoretical analysis perspective. Then, we discuss the application of deep learning-based speech signal processing in electronic communication, including the application of models such as Transformer, LAS (Listen, Attend and Spell), and GFT-conformer in speech signal processing. We also discuss some application scenarios of deep learning-based speech signal processing in electronic communication. Finally, we identify the need for deeper application of deep learning technology in speech signal processing and electronic communication, with continuous optimization and adjustment.

A Hidden Markov Model-Based Speech Recognition System Using Baum-Welch, Forward-Backward and Viterbi Algorithms

Speech is the most complex part or component of human intelligence and for that matter speech signal processing is very important. The variability of speech is very high, and this makes speech recognition difficult. Other factors like dialects, speech duration, context dependency, different speech speed, speaker differentiation, environment and locality all add to the difficulty in speech processing. The absence of distinct boundaries between tones or words causes additional problems. Speech has speaker dependent characteristics, so that no one can reproduce or repeat phrases in the same way as another. Nevertheless, a speech recognition system should be able to model and recognize the same words and phrases absolutely. Digital signal processors (DSP) are often used in speech signal processing systems to control these complexities. This paper presents a Hidden Markov Model (HMM) based speech signal modulation through the application of the Baum-Welch, Forward-Backward and Viterbi algorithms. The system was implemented using a 16-bit floating point DSP (TMS320C6701) from Texas instruments and the vocabulary was trained using the Microsoft Hidden Markov Model Toolkit (HTK). The proposed system achieved about 79% correct word recognition which represents approximately 11,804 correct words recognized out of a total of 14960 words provided. This result indicates that the proposed model accuracy and speaker independent system has a very good evaluation score, and thus can be used to aid dictation for speech impaired persons and applications in real time with a 10 ms data exchange rate.

Implementation of Hybrid Deep Reinforcement Learning Technique for Speech Signal Classification

Classification of speech signals is a vital part of speech signal processing systems. With the advent of speech coding and synthesis, the classification of the speech signal is made accurate and faster. Conventional methods are considered inaccurate due to the uncertainty and diversity of speech signals in the case of real speech signal classification. In this paper, we use efficient speech signal classification using a series of neural network classifiers with reinforcement learning operations. Prior classification of speech signals, the study extracts the essential features from the speech signal using Cepstral Analysis. The features are extracted by converting the speech waveform to a parametric representation to obtain a relatively minimized data rate. Hence to improve the precision of classification, Generative Adversarial Networks are used and it tends to classify the speech signal after the extraction of features from the speech signal using the cepstral coefficient. The classifiers are trained with these features initially and the best classifier is chosen to perform the task of classification on new datasets. The validation of testing sets is evaluated using RL that provides feedback to Classifiers. Finally, at the user interface, the signals are played by decoding the signal after being retrieved from the classifier back based on the input query. The results are evaluated in the form of accuracy, recall, precision, f-measure, and error rate, where generative adversarial network attains an increased accuracy rate than other methods: Multi-Layer Perceptron, Recurrent Neural Networks, Deep belief Networks, and Convolutional Neural Networks.

Speech Dereverberation Based on Improved Wasserstein Generative Adversarial Networks

In reality, the sound we hear is not only disturbed by noise, but also the reverberant, whose effects are rarely taken into account. Recently, deep learning has shown great advantages in speech signal processing. But among the existing dereverberation approaches, very few methods apply deep learning at the waveform level. In addition, in the case of sever reverberation, the conventional speech dereverberation methods perform poorly, such as MCLP (multi-channel linear prediction). We proposed a new speech dereverberation method in this paper, which is based on improved WGAN (Wasserstein Generative Adversarial Networks), called WGAN-GP, whose generator uses strided-convolutional networks and the discriminator is structured on DNNs. Due to the addition of the gradient penalty item, WGAN-GP improves the stability of training and the generalization of the model. In the case of severe reverberation, according to the experimental results, the proposed system can perform better than MCLP. As the proposed method based on WAGN-GP can improve speech quality, speech signal processing systems may be able to apply it to pre-processing stage.

Robust speech recognition system using multi-parameter bidirectional Kalman filter

A speech signal processing system using multi-parameter model bidirectional Kalman filter has been proposed in this paper. Conventional unidirectional Kalman filter usually performs estimation of current state speech signal by processing the time varying autoregressive model of speech signals from the past time states. A bidirectional Kalman filter utilizes the past and future measurements to estimate the current state of a speech signal that minimize the mean of the squared error using efficient recursive means. The matrices involved in the difference equations and the measurement equations of the bidirectional Kalman filter algorithm are kept constant throughout the process. With multi-parameter model, the proposed bidirectional Kalman filter relates more measurements from the future and past time states to the current time state. The proposed multi-parameter bidirectional Kalman filter has been implemented into a speech recognition system and its performance has been compared to other conventional speech processing algorithms. Compared to the single-parameter model bidirectional Kalman filter, the multi-parameter bidirectional Kalman filter improves the accuracy in the state prediction, reduces the speech information lost after the filtering process and better word error rate has been achieved at high SNR regions (clean, 20, 15, 10 dB).

Research and Implementation of Children's Speech Signal Processing System.

As people's living standard and the degree of mass culture have been constantly improved, many families are caring more about the healthy growth of early childhood. In this paper, based on the research of domestic and foreign experts and scholars: the guardians (such as parents) take appropriate intervention on children at the early stage can effectively promote children's language and cognitive ability development, and the intervention has obvious effect on the autistic spectrum disorders of children. This paper presents a system for analyzing children's speech signal, calculating the guardian’s language words, the number of children‘s verbal words and the number of guardian and children's dialogue rotation times by voice signal processing and pattern recognition technology. And related personnels use these indicators to analysis the development of children's language and cognitive ability, then adopting appropriate measures for children and providing the basis for decision-making criteria, so as to promote the development of children's language and cognitive status.

Retraction Notice: Research and Implementation of Children's Speech Signal Processing System.

[This retracts the article on p. 188 in vol. 9 PMC6407307.].

Audio-visual voice activity detection

In speech signal processing systems, frame-energy based voice activity detection (VAD) method may be interfered with the background noise and non-stationary characteristic of the frame-energy in voice segment. The purpose of this paper is to improve the performance and robustness of VAD by introducing visual information. Meanwhile, data-driven linear transformation is adopted in visual feature extraction, and a general statistical VAD model is designed. Using the general model and a two-stage fusion strategy presented in this paper, a concrete multimodal VAD system is built. Experiments show that a 55.0 % relative reduction in frame error rate and a 98.5% relative reduction in sentence-breaking error rate are obtained when using multimodal VAD, compared to frame-energy based audio VAD. The results show that using multimodal method, sentence-breaking errors are almost avoided, and frame-detection performance is clearly improved, which proves the effectiveness of the visual modal in VAD.

Speech signal processing system

A speech signal processing system in which the correlation is removed from the sample values of a speech waveform supplied to an inverse-filter for obtaining sample values of a prediction residual waveform, phase-equalizing filter coefficients are determined to have phase-characteristic inverse to that of the prediction residual waveform at each pitch position of the speech waveform, the phase-equalizing filter coefficients are set as filter coefficients of the phase-equalizing filter, and the speech waveform or the prediction residual waveform is passed through the phase-equalizing filter, thereby zero-phasing the prediction residual waveform or the prediction residual waveform component in the speech waveform and concentrating energy around the pitch position.