Whispered Speech Recognition Using Deep Denoising Autoencoder and Inverse Filtering

Grozdic, Dorde T.; Jovičić, Slobodan

doi:10.1109/taslp.2017.2738559

Cited by 58 publications

(20 citation statements)

References 21 publications

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“…They took into consideration a pronunciation model since words may have multiple correct pronunciations, as they are influenced by factors such as the speaker's accent, speaking style, and neighboring words. The authors of [55] presented vocal characteristics of whispered speech and discussed the problems for the recognition of whispered speech in different conditions. They provided a new pre-process method with cepstral features based on a deep denoising autoencoder (DDAE) to improve whisper recognition.…”

Section: ) Pronunciationmentioning

confidence: 99%

Automatic Speech Recognition: Systematic Literature Review

et al. 2021

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A huge amount of research has been done in the field of speech signal processing in recent years. In particular, there has been increasing interest in the automatic speech recognition (ASR) technology field. ASR began with simple systems that responded to a limited number of sounds and has evolved into sophisticated systems that respond fluently to natural language. This systematic review of automatic speech recognition is provided to help other researchers with the most significant topics published in the last six years. This research will also help in identifying recent major ASR challenges in real-world environments. In addition, it discusses current research gaps in ASR. This review covers articles available in five research databases that were completed according to the preferred reporting items for systematic reviews and metaanalyses (PRISMA) protocol. The search strategy yielded 45 articles related to the study's scope for the period 2015-2020. The results presented in this review shed light on research trends in the area of ASR and also suggest new research directions.

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Section: ) Pronunciationmentioning

confidence: 99%

Automatic Speech Recognition: Systematic Literature Review

et al. 2021

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“…In classification tasks, it is essential to obtain a set of features that helps to translate meaningful information accurately to discriminate the respective identities of the features [ 2 , 31 , 32 ]. In human speech recognition applications, extracted formant coefficients are often harvested and manipulated and then used as features [ 33 , 34 , 35 , 36 ]. Speech recognition processes in the present-day scenarios have reached a level of maturity where they are able to produce exceptional performance.…”

Section: Spectral Entropy Features and Classification Frameworkmentioning

confidence: 99%

Texture Classification Using Spectral Entropy of Acoustic Signal Generated by a Human Echolocator

Abdullah

Saleh

Rahman

et al. 2019

Entropy

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Human echolocation is a biological process wherein the human emits a punctuated acoustic signal, and the ear analyzes the echo in order to perceive the surroundings. The peculiar acoustic signal is normally produced by clicking inside the mouth. This paper utilized this unique acoustic signal from a human echolocator as a source of transmitted signal in a synthetic human echolocation technique. Thus, the aim of the paper was to extract information from the echo signal and develop a classification scheme to identify signals reflected from different textures at various distance. The scheme was based on spectral entropy extracted from Mel-scale filtering output in the Mel-frequency cepstrum coefficient of a reflected echo signal. The classification process involved data mining, features extraction, clustering, and classifier validation. The reflected echo signals were obtained via an experimental setup resembling a human echolocation scenario, configured for synthetic data collection. Unlike in typical speech signals, extracted entropy from the formant characteristics was likely not visible for the human mouth-click signals. Instead, multiple peak spectral features derived from the synthesis signal of the mouth-click were assumed as the entropy obtained from the Mel-scale filtering output. To realize the classification process, K-means clustering and K-nearest neighbor processes were employed. Moreover, the impacts of sound propagation toward the extracted spectral entropy used in the classification outcome were also investigated. The outcomes of the classifier performance herein indicated that spectral entropy is essential for human echolocation.

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“…Whispered speech is a method of articulation different from normal speech [1]; it is produced without vibration of the vocal cords at a low sound level, which causes the voiced sound of whispered speech to have no fundamental frequency and an energy 20 dB less than that of normal speech [2]. Because of these characteristics, whispered speech is widely used in places where loud noises are prohibited such as conference rooms, libraries, and concert halls.…”

Section: Introductionmentioning

confidence: 99%

Whispered Speech Conversion Based on the Inversion of Mel Frequency Cepstral Coefficient Features

et al. 2022

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A conversion method based on the inversion of Mel frequency cepstral coefficient (MFCC) features was proposed to convert whispered speech into normal speech. First, the MFCC features of whispered speech and normal speech were extracted and a matching relation between the MFCC feature parameters of whispered speech and normal speech was developed through the Gaussian mixture model (GMM). Then, the MFCC feature parameters of normal speech corresponding to whispered speech were obtained based on the GMM and, finally, whispered speech was converted into normal speech through the inversion of MFCC features. The experimental results showed that the cepstral distortion (CD) of the normal speech converted by the proposed method was 21% less than that of the normal speech converted by the linear predictive coefficient (LPC) features, the mean opinion score (MOS) was 3.56, and a satisfactory outcome in both intelligibility and sound quality was achieved.

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Whispered Speech Recognition Using Deep Denoising Autoencoder and Inverse Filtering

Cited by 58 publications

References 21 publications

Automatic Speech Recognition: Systematic Literature Review

Automatic Speech Recognition: Systematic Literature Review

Texture Classification Using Spectral Entropy of Acoustic Signal Generated by a Human Echolocator

Whispered Speech Conversion Based on the Inversion of Mel Frequency Cepstral Coefficient Features

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