Cognitive Speech Coding: Examining the Impact of Cognitive Speech Processing on Speech Compression

Cerňak, Miloš; Asaei, Afsaneh; Hyafil, Alexandre

doi:10.1109/msp.2017.2761895

Cited by 19 publications

(12 citation statements)

References 79 publications

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“…However, the significant development of computer technology in the last decade has enabled research into new approaches to advanced speech signal processing including adaptive machine learning methods [14]. Recent trends include cognitive speech coding so that there is a paradigm shift from perceptual (auditory) toward cognitive (auditory plus cortical) speech signal processing [15].…”

Section: Introductionmentioning

confidence: 99%

Speech Technology Progress Based on New Machine Learning Paradigm

Delić

Perić

Secujski

et al. 2019

Computational Intelligence and Neuroscience

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Speech technologies have been developed for decades as a typical signal processing area, while the last decade has brought a huge progress based on new machine learning paradigms. Owing not only to their intrinsic complexity but also to their relation with cognitive sciences, speech technologies are now viewed as a prime example of interdisciplinary knowledge area. This review article on speech signal analysis and processing, corresponding machine learning algorithms, and applied computational intelligence aims to give an insight into several fields, covering speech production and auditory perception, cognitive aspects of speech communication and language understanding, both speech recognition and text-to-speech synthesis in more details, and consequently the main directions in development of spoken dialogue systems. Additionally, the article discusses the concepts and recent advances in speech signal compression, coding, and transmission, including cognitive speech coding. To conclude, the main intention of this article is to highlight recent achievements and challenges based on new machine learning paradigms that, over the last decade, had an immense impact in the field of speech signal processing.

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Section: Introductionmentioning

confidence: 99%

Speech Technology Progress Based on New Machine Learning Paradigm

Delić

Perić

Secujski

et al. 2019

Computational Intelligence and Neuroscience

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“…The third stage is the speech coding stage. In this stage, the speaker gradually converts the semantic grammatical information into speech coding, which is finally executed by the motor system [14][15] [16].…”

Section: Dysarthriamentioning

confidence: 99%

Automatic Assessment of Dysarthric Severity Level Using Audio-Video Cross-Modal Approach in Deep Learning

Han¹,

Sharifzadeh

McLoughlin

2020

Interspeech 2020

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Dysarthria is a speech disorder disease that can have a significant impact on a person's daily life. Early detection of the disease can put the patient into therapy sessions more quickly. Researchers have established various approaches to detect the disease automatically. Traditional computational approaches commonly analysed acoustic features like Mel-Frequency Cepstral Coefficients (MFCC), Spectral Centroid, Linear Prediction Cepstral (LPC) coefficients and Perceptual Linear Prediction (PLP) from speech samples of patients to detect dysarthric speech characters like slow speech rate, short pauses, mis-articulated sounds, etc. Recent research has shown that some machine learning algorithms can also be deployed to extract speech features and detect the severity level automatically.

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“…Since converted speech will be listened to by a human, it is highly desirable to adopt human auditory-based distance as an objective function to be minimized. Such a distance measure has already been used for various forms of speech processing procedures, such as speech enhancement [25], speech recognition [26], speech coding [27], speech synthesis [28,29] and speech quality evaluation [30]. This distance measure has not yet been adapted as an objective metric in VC.…”

Section: Introductionmentioning

confidence: 99%

Voice Conversion Using a Perceptual Criterion

Lee

2020

Applied Sciences

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In voice conversion (VC), it is highly desirable to obtain transformed speech signals that are perceptually close to a target speaker’s voice. To this end, a perceptually meaningful criterion where the human auditory system was taken into consideration in measuring the distances between the converted and the target voices was adopted in the proposed VC scheme. The conversion rules for the features associated with the spectral envelope and the pitch modification factor were jointly constructed so that perceptual distance measurement was minimized. This minimization problem was solved using a deep neural network (DNN) framework where input features and target features were derived from source speech signals and time-aligned version of target speech signals, respectively. The validation tests were carried out for the CMU ARCTIC database to evaluate the effectiveness of the proposed method, especially in terms of perceptual quality. The experimental results showed that the proposed method yielded perceptually preferred results compared with independent conversion using conventional mean-square error (MSE) criterion. The maximum improvement in perceptual evaluation of speech quality (PESQ) was 0.312, compared with the conventional VC method.

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Cognitive Speech Coding: Examining the Impact of Cognitive Speech Processing on Speech Compression

Cited by 19 publications

References 79 publications

Speech Technology Progress Based on New Machine Learning Paradigm

Speech Technology Progress Based on New Machine Learning Paradigm

Automatic Assessment of Dysarthric Severity Level Using Audio-Video Cross-Modal Approach in Deep Learning

Voice Conversion Using a Perceptual Criterion

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