Intelligibility Enhancement Via Normal-to-Lombard Speech Conversion With Long Short-Term Memory Network and Bayesian Gaussian Mixture Model

Li, Gang; Wang, Xiaochen; Hu, Ruimin; Zhang, Huyin; Ke, Shanfa

doi:10.1109/tmm.2021.3068565

Cited by 3 publications

(2 citation statements)

References 39 publications

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“…Compared with previous studies, the deep neural network is used to map high dimensions instead of Gaussian model, and tilt correction module is creatively added to further reduce the mapping error of formant amplitude. Considering the short-term correlation of voice and the accuracy of spectral slope mapping, they also proposed an intelligibility enhancement algorithm based on LSTM and BGMM for the change from normal speech to lombard speech [21] . Using LSTM to map the first 20-dim MCC features to characterize the spectral tilt more accurately.…”

Section: Transformation Methods Based On Feature Mappingmentioning

confidence: 99%

Compensation of Lombard effect in speech enhancement

2023

Third International Conference on Digital Signal and Computer Communications (DSCC 2023)

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Influenced by Lombard effect, the synthetic noisy speech training data commonly used in speech enhancement algorithm based on deep learning is different from the pronunciation under actual noise, which leads to the limited application performance of the algorithm in the actual environment. This paper summarizes the data compensation strategy proposed by researchers for speech enhancement algorithm under Lombard effect. However, these compensation strategies mainly focus on the front-end data processing part of speech enhancement, which improves the complexity of speech enhancement system.

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Section: Transformation Methods Based On Feature Mappingmentioning

confidence: 99%

Compensation of Lombard effect in speech enhancement

2023

Third International Conference on Digital Signal and Computer Communications (DSCC 2023)

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“…Yan et al used LSTM to construct an end-to-end neural network framework for machine translation and introduced local attention mechanism into the model to improve translation quality [15]. Li et al used the reinforcement learning actor-critic training network to evaluate the value of the output notes of LSTM network, so as to update the generation strategy of LSTM network, and the generated music has a stable structure and more style [16]. Saqib et al conducted two experiments on two-way LSTM and oneway LSTM on speech corpus, and found that two-way LSTM was superior to one-way LSTM and conventional RNN [17].…”

Section: Research Status Of Lstmmentioning

confidence: 99%

Musical Note Feature Recognition Based on Long Short-Term Memory

Yuan

2022

Mobile Information Systems

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The timbre and volume of a single tone are among its fundamental characteristics. The single-tone detection technology is the key to the foundation of MNFR (musical note feature recognition), which is built on the fundamental feature extraction of single tones. A MNFR method based on LSTM (long short-term memory) is proposed because traditional methods have low accuracy in note feature classification and low accuracy in MNFR. To process the series of convolution feature maps, the feature maps are directly input into LSTM to learn hash codes. Extract the note features. Segment according to the changing trend of the physical features of the notes. Additionally, a number of feature maps are built from convolution feature maps extracted from numerous convolution layers of previously trained CNNs (convolutional neural networks), taking into account the spatial specifics and semantic features. The note start vector is produced using the enhanced peak extraction algorithm based on Gaussian kernel smoothing. The findings indicate that, when 100 samples are used, this method’s note classification accuracy differs from that of the DBN (deep belief network) and DWT (discrete wavelet transform) by 1.17 percent and 2.04 percent, respectively. The analysis in the conclusion demonstrates that the algorithm put forth in this paper is both theoretically and practically workable.

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Lombard effect in Algerian Dialect Speech: Investigation of boosting and bypass strategies in narrowband noisy environment

Ykhlef

Bouchaffra

2022

Applied Acoustics

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Intelligibility Enhancement Via Normal-to-Lombard Speech Conversion With Long Short-Term Memory Network and Bayesian Gaussian Mixture Model

Cited by 3 publications

References 39 publications

Compensation of Lombard effect in speech enhancement

Compensation of Lombard effect in speech enhancement

Musical Note Feature Recognition Based on Long Short-Term Memory

Lombard effect in Algerian Dialect Speech: Investigation of boosting and bypass strategies in narrowband noisy environment

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