Deep residual networks for pre-classification based Indian language identification

Bhanja, Chuya China; Bisharad, Dipjyoti; Laskar, Rabul Hussain

doi:10.3233/jifs-169932

Cited by 14 publications

(5 citation statements)

References 16 publications

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“…Driven by technology, there will be new interpretations [3]. With the vigorous promotion of network technology, the amount of online music data is increasing day by day, and the demand for analysis, retrieval, and processing of music information has become increasingly prominent [4]. As one of the hotspots in the field of signal and information processing, music separation is an important part of music technology research [5].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] The New Approach Research on Singing Voice Detection Algorithm Based on Enhanced Reconstruction Residual Network

Liu

2022

Journal of Mathematics

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With the development of Internet technology, multimedia information resources are increasing rapidly. Faced with the massive resources in the multimedia music library, it is extremely difficult for people to find the target music that meets their needs. How to realize computer analysis and perceive users’ needs for music resources has become the goal of the future development of human-computer interaction capabilities. Content-based music information retrieval applications are mainly embodied in the automatic classification and recognition of music. Traditional feedforward neural networks are prone to lose local information when extracting singing voice features. For this reason, on the basis of fully considering the impact of information persistence in the network propagation process, this paper proposes an enhanced two-stage super-resolution reconstruction residual network which can effectively integrate the learned features of each layer while increasing the depth of the network. The first stage of reconstruction is to complete the hierarchical learning of singing voice features through dense residual units to improve the integration of information. The second stage of reconstruction is mainly to perform residual relearning on the high-frequency information of the singing voice learned in the first stage to reduce the reconstruction error. In the middle of these two stages, the model introduces feature scaling and expansion convolution to achieve the dual purpose of reducing information redundancy and increasing the receptive field of the convolution kernel. A monophonic singing voice separation based on the high-resolution neural network is proposed. Because the high-resolution network has parallel subnetworks with different resolutions, it also has original resolution representations and multiple low-resolution representations, avoiding information loss caused by serial network downsampling effects and repeating multiple feature fusions to generate new semantic representations, allowing for the learning of comprehensive, high-precision, and highly abstract features. In this article, a high-resolution neural network is utilized to model the time spectrogram in order to correctly estimate the real value of the anticipated time-amplitude spectrograms. Experiments on the dataset MIR-1K show that compared with the current leading SH-4Stack model, the method in this paper has improved SDR, SIR, and SAR indicators for measuring the separation performance, confirming the effectiveness of the algorithm in this paper.

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Section: Introductionmentioning

confidence: 99%

[Retracted] The New Approach Research on Singing Voice Detection Algorithm Based on Enhanced Reconstruction Residual Network

Liu

2022

Journal of Mathematics

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“…Different sets of features [6, 8] are used for LID. LID is done by deep residual networks [10]. Language recognition [11] is done by using hybrid robust feature extraction techniques.…”

Section: Analysis Of Speech Uttered In Different Languagesmentioning

confidence: 99%

Real Time Raspberry Pi based System for Linguistic Content Recognition from Speech

2023

Preprint

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Recognizing linguistic information from speech has found applications in interpretation of language in which the utterance is spoken and the system could be used as a translator to convert sentence spoken in one language into another language meaningfully. Real time implementation of language identification (LID) from speech requires the speech to be fed from the Raspberry Pi board used in the transmitter section and the Raspberry Pi board in the receiver section receives it and given to the system for identifying the language of the speech. This system requires the training phase in which two dimensional spectrogram features are derived from the training set of speeches and given to the CNN layered architecture for creating templates for languages. Testing phase involves the transmission of speech from the memory card of the Raspberry Pi board in transmitter system. Raspberry Pi board in the receiver receives it and given to the system in receiver section. Two dimensional spectrogram features are derived for test speech and given to the CNN templates and based on the similarity index, test language is interpreted. This system is implemented using spectrogram, Melspectrogram and ERB spectrogram as features and CNN for modeling and classification of languages. Validation error is 1.4%, 1.8% and 3% for spectrogram, Melspectrogram and ERB spectrogram based systems respectively and decision level fusion classifier gives 0.9% as validation error. This system can be implemented in hardware by using Raspberry Pi board. This automated real time multilingual language identification system would be useful in forensic department and defense sectors to identify the persons belonging to any region or speaking in any language.

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“…The author proposed adversarial learning which was used to ensure that the shared layers of the SHL-Model would learn more language invariant features. Bhanja et al [31] investigated the language discriminating ability of various acoustic features like pitch Chroma, mel-frequency Cepstral coefficients (MFCCs), and their combination. The system performance has been analyzed for features extracted using different analysis units, like, syllables and utterances.…”

Section: Marathimentioning

confidence: 99%

Computational intelligence in processing of speech acoustics: a survey

Singh

Kaur

Kukreja

et al. 2022

Complex Intell. Syst.

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Speech recognition of a language is a key area in the field of pattern recognition. This paper presents a comprehensive survey on the speech recognition techniques for non-Indian and Indian languages, and compiled some of the computational models used for processing speech acoustics. An immense number of frameworks are available for speech processing and recognition for languages persisting around the globe. However, a limited number of automatic speech recognition systems are available for commercial use. The gap between the languages being spoken around the globe and the technical support available to these languages are very few. This paper examined major challenges for speech recognition for different languages. Analysis of the literature shows that lack of standard databases availability of minority languages hinder the research recognition research across the globe. When compared with non-Indian languages, the research on speech recognition of Indian languages (except Hindi) has not achieved the expected milestone yet. Combination of MFCC and DNN–HMM classifier is most commonly used system for developing ASR minority languages, whereas in some of the majority languages, researchers are using much advance algorithms of DNN. It has also been observed that the research in this field is quite thin and still more research needs to be carried out, particularly in the case of minority languages.

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Deep residual networks for pre-classification based Indian language identification

Cited by 14 publications

References 16 publications

[Retracted] The New Approach Research on Singing Voice Detection Algorithm Based on Enhanced Reconstruction Residual Network

[Retracted] The New Approach Research on Singing Voice Detection Algorithm Based on Enhanced Reconstruction Residual Network

Real Time Raspberry Pi based System for Linguistic Content Recognition from Speech

Computational intelligence in processing of speech acoustics: a survey

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