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Speech

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Deep neural network-based generalized sidelobe canceller for dual-channel far-field speech recognition.

Neural networks : the official journal of the International Neural Network Society
The traditional generalized sidelobe canceller (GSC) is a common speech enhancement front end to improve the noise robustness of automatic speech recognition (ASR) systems in the far-field cases. However, the traditional GSC is optimized based on the...

What Can Network Science Tell Us About Phonology and Language Processing?

Topics in cognitive science
Contemporary psycholinguistic models place significant emphasis on the cognitive processes involved in the acquisition, recognition, and production of language but neglect many issues related to the representation of language-related information in t...

Improving Loanword Identification in Low-Resource Language with Data Augmentation and Multiple Feature Fusion.

Computational intelligence and neuroscience
Loanword identification is studied in recent years to alleviate data sparseness in several natural language processing (NLP) tasks, such as machine translation, cross-lingual information retrieval, and so on. However, recent studies on this topic usu...

Deep learning architectures for estimating breathing signal and respiratory parameters from speech recordings.

Neural networks : the official journal of the International Neural Network Society
Respiration is an essential and primary mechanism for speech production. We first inhale and then produce speech while exhaling. When we run out of breath, we stop speaking and inhale. Though this process is involuntary, speech production involves a ...

Deep ANC: A deep learning approach to active noise control.

Neural networks : the official journal of the International Neural Network Society
Traditional active noise control (ANC) methods are based on adaptive signal processing with the least mean square algorithm as the foundation. They are linear systems and do not perform satisfactorily in the presence of nonlinear distortions. In this...

Combining a parallel 2D CNN with a self-attention Dilated Residual Network for CTC-based discrete speech emotion recognition.

Neural networks : the official journal of the International Neural Network Society
A challenging issue in the field of the automatic recognition of emotion from speech is the efficient modelling of long temporal contexts. Moreover, when incorporating long-term temporal dependencies between features, recurrent neural network (RNN) a...

Residual Neural Network precisely quantifies dysarthria severity-level based on short-duration speech segments.

Neural networks : the official journal of the International Neural Network Society
Recently, we have witnessed Deep Learning methodologies gaining significant attention for severity-based classification of dysarthric speech. Detecting dysarthria, quantifying its severity, are of paramount importance in various real-life application...

Multi-Path and Group-Loss-Based Network for Speech Emotion Recognition in Multi-Domain Datasets.

Sensors (Basel, Switzerland)
Speech emotion recognition (SER) is a natural method of recognizing individual emotions in everyday life. To distribute SER models to real-world applications, some key challenges must be overcome, such as the lack of datasets tagged with emotion labe...

Human cortical encoding of pitch in tonal and non-tonal languages.

Nature communications
Languages can use a common repertoire of vocal sounds to signify distinct meanings. In tonal languages, such as Mandarin Chinese, pitch contours of syllables distinguish one word from another, whereas in non-tonal languages, such as English, pitch is...

Biosignal Sensors and Deep Learning-Based Speech Recognition: A Review.

Sensors (Basel, Switzerland)
Voice is one of the essential mechanisms for communicating and expressing one's intentions as a human being. There are several causes of voice inability, including disease, accident, vocal abuse, medical surgery, ageing, and environmental pollution, ...