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Articulation disorder, as a medical difficulty, currently mainstream speech recognition technologies are not well adapted to the needs of this field. At the same time, speech recognition technology for dysarthria utilizes a combination of pre training and personalized training to further improve algorithm performance and reduce recognition word error rate through data-driven methods. However, currently, speech recognition technology for dysarthria still has a certain distance from practical commercial use, and its development is limited by data scale and technology. So far, there have been no comprehensive articles on speech recognition for dysarthria. It is urgent to compare and analyze the construction methods and advanced technologies of various datasets in this field, in order to facilitate researchers entering the field to quickly acquire knowledge in this field. This paper conducts a survey on existing datasets, mainstream algorithms, and evaluation methods, and summarizes the scale, form, and characteristics of mainstream speech impairment datasets at home and abroad. It analyzes the mainstream algorithms for speech recognition with dysarthria, and provides the performance and characteristics of different algorithms. Finally, the performance evaluation indicators of the algorithm model based on the severity level of patients with dysarthria are studied, and future research directions are discussed, in order to provide help for the researchers engaged in speech recognition with dysarthria and assist in the rapid development of this field. [ABSTRACT FROM AUTHOR]

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To solve the problems that the Attention's overly flexible alignment was poorly adaptable in complex environments and the language features were not fully utilized by simple end-to-end models, a speech recognition method was investigated based on multi-task loss with additional language model. By analyzing the characteristics of the speech signal, the features containing more information were selected in the training. Based on the Attention-based Conformer end-to-end model, the model was trained using multi-task loss of CTC loss assisted pure Conformer (Attention), and the Conformer-CTC speech recognition model was obtained. Based on the Conformer-CTC model, by analyzing and comparing the characteristics and effects of some language models, the Transformer language model was added to the training of the above model through re-scoring mechanism, and the Conformer-CTC-Transformer speech recognition model was obtained. The experiments on the above model were completed on the AISHELL-1 data set. The results show that compared with the pure Conformer (Attention) model, the character error rate (CER) of the Conformer-CTC model on the test set is reduced by 0. 49%, and the CER of the Conformer-CTC-Transformer model on the test set is reduced by 0.79% compared with the Conformer- CTC model. The adaptability of Attention alignment in complex environments can be improved by CTC loss, and after re-scoring the Transformer-CTC model with the Transformer language model, the recognition accuracy can be increased by 0. 30% again. Compared with some existing end-to-end models, the recognition effect of the Conformer-CTC-Transformer model is better, indicating that the model has certain effectiveness. [ABSTRACT FROM AUTHOR]

Speech recognition technology is a key component of the intelligent voice interaction system. In recent years, this technology has developed rapidly and is widely used in automotive electronics, consumer electronics, medical and other fields. This paper analyzes the development status of speech technology in the field of automotive field, introduces the development process of this technology in detail, focuses on the demand and application of speech recognition technology in the field of rail transit.Aiming at the problem that the display interactive content of different models of rail transit is changeable and needs to be customized and developed, the research on speech recognition technology based on time-delay neural network is carried out and a speech recognition system for train display based on intelligent computing platform is developed. The system can flexibly build models according to the interaction requirements of different display platforms and the processing performance of intelligent hardware platforms, and optimize the interaction keywords on the basis of the general model to improve the recognition rate. Finally, the test is completed in the voice interaction system of autonomous-rail rapid tram display in different scenes and noisy conditions and the word recognition rate reaches more than 85%. It also lays the foundation for the application of intelligent voice technology in other rail transit vehicles.

In view of the problem that traditional sorting manipulator lacks perceptual ability, and most of them are rigid grasping, leading to inadequate adaptability of operation objects (such as fruits and other agricultural products), a multi-sensory flexible sorting system with auditory, visual and tactile functions was designed. Based on the structure and kinematics analysis of the connecting rod serial manipulator, the overall design scheme of the system was proposed. The hardware and software were designed for intelligent speech recognition module, vision module based on deep learning, pressure detection module with ATMEGA328P single chip microcomputer as the core controller. A sorting test was carried out with apple, kiwi, orange and pear as the target objects. The results show that the system can receive non-specific voice demands through human-machine interaction. And the robot arm moves and positions itself under the guidance of vision, grabs various target objects adaptively, and performs the sorting task accurately. The positioning accuracy of the system is 0.3 mm, and the average accuracy of sorting is 92%, which proves the system to be intelligent and flexible. The study provides reference for improving the intelligent level and environmental adaptability of the sorting system. [ABSTRACT FROM AUTHOR]

As a new deep learning algorithm framework, Transformer has attracted more and more researchers?? attention and has become a current research hotspot. Inspired by humans focusing on important things only, the self-attention mechanism in the Transformer model mainly learns important information in the input sequence. For speech recogni-tion tasks, the focus is to transcribe the information of the input speech sequence into the corresponding language text. The past practice was to combine acoustic models, pronunciation dictionaries, and language models into a speech recognition system to achieve speech recognition tasks, while Transformer can integrate them into a single neural network to form an end-to-end speech recognition system, which solves the issues such as forced alignment and multi-module training of the traditional speech recognition system. Therefore, it is very necessary to discuss the problems of Transformer in speech recognition tasks. In this paper, the structure of the Transformer model is first introduced. Besides, the problems confronted by speech recognition are analyzed with respect to input speech sequence, deep model architecture, and model inference. Then the methods to solve the obstacles within the three aspects afore mentioned are outlined and summarized. Finally, the future application and direction of Transformer in speech recognition are concluded and prospected.

In recent years, deep learning has made a breakthrough in the field of speech recognition, and pushes forward the wide application of speech recognition technology in people's daily lives. Further optimization of the speech recognition model needs to be supported by a larger scale calibrated data. However, the scale of the current open source audio data set is still too small, and corpus is mostly written language of news-based long texts. This paper, by talking about the popular speech recognition applications like human-computer interaction and intelligent customer service, builds and opens the largest ever Chinese Mandarin speech corpus DTZH1505 through crowdsourcing. Data set records natural speech of 6 408 speakers from 8 major Chinese dialect regions and 33 provinces, up to 1 505 hours and on various scenes like social networking, human-computer interaction, intelligent customer service and on-board commands. It can be widely used in the researches of corpus linguistics, conversation analysis, speech recognition, as well as speaker recognition. This paper implements a series benchmark speech recognition experiments, and the results show that: compared to the same scale Chinese speech corpus aishell2, the speech recognition model based on this data set has better performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To improve the performance of multi-accent Mandarin speech recognition task,this paper presented a method for hybrid end-to-end automatic speech recognition(ASR) by combining CTC and multi-head attention by using a multiobjective training and joint decoding.The analysis shows that hybrid model with lower CTC weight and deeper encoder layers performance better learning capacity.And it trained a very deep models with up to 48 layers for encode-decoder architecture,which outperforms all previous end-to-end ASR approaches on Aidatatang 200 h multi-accent dataset,achieves 5.6% character error rate(CER) and 26.2% sentence error rate(SER).The experiment proves that the recognition rate of the end-to-end model proposed exceeds the general end-to-end model,and it has certain advancedness in solving the Mandarin recognition with accents. [ABSTRACT FROM AUTHOR]

In the era of big data, many of traditional machine learning methods of disposing unlabeled raw voice data have become less applicable. At the same time, deep learning models can directly process unlabeled data because of its powerful capability of modeling to deal with the massive data, and has become a hot research in the field of speech recognition. To begin with, this paper analyzed and summarized the state-of-the-art deep learning of models. And then,it discussed the applications to speech recognition with speech features extraction and acoustic modeling. Finally,it concluded the problems faced and development orientation. [ABSTRACT FROM AUTHOR]

To improve the performance of the conventional speech recognition system, this paper introduced the autoencoder deep learning neural networks which was applied to speech recognition. The neural networks based on deep learning introduced greedy layer-wise learning algorithm by pretraining and fine-tuning. It could extract the essential features of speech signal which was needed to recognition. It could overcome the shortcomings of the conventional multilayer artificial neural networks which easily trapped into local optimum when training the model. And they needed a large number of labeled data. Then the structured alignment networks could align arbitrary frames of features to fixed frames. And it input these features to a classifier to speech recognition. This paper did some experiment with back propagation neural networks and autoencoder neural networks respectively. The results illustrate that the deep learning neural networks can outperform the conventional neural networks by 20.0% in accuracy. It is an excellent speech recognition model. [ABSTRACT FROM AUTHOR]