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Cross-lingual speech recognition leverages data from a variety of source languages to train systems capable of identifying speech in a target language, thus promoting intercultural communication and understanding. To address the issues of how to utilize multilingual data to improve the recognition performance of low resource languages in cross-lingual speech recognition, domain shift or interference between source and target languages, task weights and data distribution between different languages, a cross lingual speech recognition model was studied through feature prompts. To simplify the traditional process of requiring professionals to label phonemes uniformly, a cross-lingual speech data annotation method was studied by identifying the corresponding language in the original data, and experiments were conducted on two public datasets. The results show that the proposed model achieves a substantial reduction in the average error rate 46.44% lower than the Conformer model, a mainstream speech recognition model, and 2.1% lower than the baseline model, thereby attaining higher accuracy in recognition. The research results provide novel perspectives and methodologies for the domain of cross-lingual speech recognition. [ABSTRACT FROM AUTHOR]

The acoustic input network based on the Conformer encoder has the problem of insufficient extraction of FBank speech information and missing channel feature information. This paper proposed an end-to-end method based on RepVGG-SE-Conformer for speech recognition to solve these problems. Firstly, the proposed model used the multi-branch structure of RepVGG to enhance the speech information extraction capability, and using the structural re-parameterization fused the multibranch into a single branch to reduce the computational complexity and speed up the model inference. Then, based on the squeeze-and-excitation network, the channel attention mechanism made up for the missing channel feature information to improve speech recognition accuracy. Finally, the experimental results on the public dataset Aishell-1 show that the proposed method's character error rate is reduced by 10.67% compared with Conformer, and the advancement of the method is verified. In addition, the proposed RepVGG-SE acoustic input network has good generalization ability in the end-to-end scene, which can effectively improve the overall performance of speech recognition models based on Transformer variants. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She 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.)

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Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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.)

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She 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.)

local feature information at shallow layers. To solve this problem, this study proposes a method using multiple encoders to improve the ability of speech feature extraction. An additional convolutional encoder branch is added to strengthen the capture of local feature information, make up for the neglect of local feature information in shallow Transformer, and effectively realize the integration of global and local dependencies of andio feature se quences. In other words, a multi-encoder model hased on Transformer is proposed. Experiments on the open source Chinese Mandarin data set Aishell 1 show that without an external language model, the proposed Transformer-based multi-encoder model has a relative reduction of 4.00% in character error rate when compared with the Transformer model. On the internal non public Shanghainese dialect data set, the performance improve ment of the proposed model is more obvious, and the character error rate is reduced by 48.24% from 19.92% to 10. 31%. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She 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.)

Due to the decrease in accuracy of speech recognition software in noisy environments, a robust enhancement method based on domain knowledge is proposed to ensure the safety of using speech control operations. Taking ship control as the application background, a domain knowledge graph is established for ship control. Ship control commands are extracted from nautical books and classic naval warfare film and television materials, and a Chinese speech dataset for ship control commands is constructed. A domain knowledge-embedded decoding method is proposed to correct the output control commands by calculating the matching degree between the recognition result and the domain knowledge graph. Experimental results show that compared with the current popular connection time sequence classification decoding method and attention mechanism-based decoding method, the proposed decoding method reduces the word error rate by 4.0% and 1.5% when recognizing noisy speech with a signal-tonoise ratio of 10dB and 20dB, respectively, and improves the accuracy of command recognition by 10.3% and 6.3%, respectively, improving the robustness of the speech recognition model in recognizing Chinese commands. [ABSTRACT FROM AUTHOR]

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]

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Recently, deep neural network model has become a hot research object in the field of speech recognition. However, the deep neural network relies on huge parameters and computational overhead, the excessively large model size also increases the difficulty of its deployment on edge devices. Aiming at the above problems, this paper proposed a lightweight speech recognition model based on Transformer. This method used depthwise separable convolution to obtain the feature information. Secondly, this method constructed a two half-step feed-forward layers, namely Macaron-N et, and introduced the lowrank matrix factorization to realize the model compression. Finally, it used a sparse attention mechanism to improve the training speed and decoding speed of the model. It tested on the Aishell-1 and aidatang_200zh datasets. The experimental results show that compared with Open-Transformer, the word error rate and real time factor of LM-Transformer decrease by 19. 8% and 32.1%, respectively. [ABSTRACT FROM AUTHOR]

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In a continuous speech recognition system, aiming at the complex environments (including the variability of speakers and environmental noise), the training data does not match the test data, which results in a low voice recognition rate. A speech recognition method based on adaptive deep neural network is studied. The improved regularized adaptive criterion and the adaptive deep neural network in the feature space are combined to improve data matching. The fusion of speaker identity vector i-vector and noise perception training are used to overcome speaker and environmental noise changes and improve the classification function of the output layer of the traditional deep neural network, which ensures the characteristics of compactness within the class and separation between classes. The test experiment was carried out by superimposing various background noises under the TIMIT English speech data set and the Microsoft Chinese speech data set. The results show that, compared with the current popular GMMHMM and traditional DNN speech acoustic models, our proposal decreases the recognition word error rate by 5.151% and 3.113% respectively, which improves the generalization performance and robustness of the model to a certain extent. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Computer Engineering & Applications 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.)

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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]

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Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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.)

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]

Based on the Mandarin pronunciation recognition technology, by the frequency selected from Mandarin speech input cavity resonance curve defined by the pickup, the Chinese word, vowel and consonant Pinyin word sequence can be identified. Combination of Chinese speech consonants, vowels and tones forms Chinese Pinyin sentence sequence. By the syllable by syllable analysis method, the frequency domain analysis technique of amplitude frequency does not need to learn or train for a particular persons voice, words and sentences. The adaptive DFT ( Discrete Fourier Transform) spectral analysis algorithm only occupies a very small amount of calculation. The method can determine the four tones of Mandarin, have fast response speed, small storage space, and can easilybe embedded to other devices. [ABSTRACT FROM AUTHOR]

Under the condition of low resource, little labeled training data is available and the performance of speech recognition system is not ideal. To solve this problem. First, this paper investigated long short term memory recurrent neural network ( LSTM RNN) for acoustic modeling. It was a powerful tool to model long time series and could make full use of the context information. Linear projection layer reduced the number of model parameters. Then, it explored speaker modeling methods in the feature space, and extracted identity vector (i-vector) which contained the speaker and channel information simultaneously. Finally, it presented a novel system, which combined the LSTM RNN model and i-vector feature. Results on the standard Open KWS 2013 data set show that this technology produces a relative improvement of about 10% in TER over the DNN baseline system. [ABSTRACT FROM AUTHOR]

To handle the speaker and noise adaptation problem in deep neural network-based speech recognition system, this paper studied the inherent characters of speaker and noise random factors and proposed a new adaptation method using long term features. Firstly, it built a joint adaptation model based on Gaussian mixture models and estimated and used the parameters of speaker and noise factors as long term features. Then, it used these long term features in deep neural network together with traditional short term features. Experiment results on Aurora4 database show that this method can effectively factorize speaker and noise factors, and improve adaptation performance. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications 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.)