Speech excitation signal recovering based on a novel error mitigation scheme under erasure channel conditions

López-Oller, Domingo; Benamirouche, Nadir; Gómez, Ángel M.; Pérez-Córdoba, José L.

doi:10.1016/j.specom.2018.01.004

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…The principle of spectral subtraction is that the power spectrum of pure speech signal can be obtained by subtracting the power spectrum of noise from the power spectrum of speech signal with noise [17].…”

Section: Spectral Subtractionmentioning

confidence: 99%

Combining Evidence from Auditory, Instantaneous Frequency and Random Forest for Anti-Noise Speech Recognition

Liao¹

2021

Computer Science and Information Technology Trends

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Due to the shortcomings of acoustic feature parameters in speech signals, and the limitations of existing acoustic features in characterizing the integrity of the speech information, This paper proposes a method for speech recognition combining cochlear feature and random forest. Environmental noise can pose a threat to the stable operation of current speech recognition systems. It is therefore essential to develop robust systems that are able to identify speech under low signal-to-noise ratio. In this paper, we propose a method of speech recognition combining spectral subtraction, auditory and energy features extraction. This method first extract novel auditory features based on cochlear filter cepstral coefficients (CFCC) and instantaneous frequency (IF), i.e., CFCCIF. Spectral subtraction is then introduced into the front end of feature extraction, and the extracted feature is called enhanced auditory features (EAF). An energy feature Teager energy operator (TEO) is also extracted, the combination of them is known as a fusion feature. Linear discriminate analysis (LDA) is then applied to feature selection and optimization of the fusion feature. Finally, random forest (RF) is used as the classifier in a non-specific persons, isolated words, and small-vocabulary speech recognition system. On the Korean isolated words database, the proposed features (i.e., EAF) after fusion with Teager energy features have shown strong robustness in the nosiy situation. Our experiments show that the optimization feature achieved in a speech recognition task display a high recognition rate and excellent anti-noise performance.

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Section: Spectral Subtractionmentioning

confidence: 99%

Combining Evidence from Auditory, Instantaneous Frequency and Random Forest for Anti-Noise Speech Recognition

Liao¹

2021

Computer Science and Information Technology Trends

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“…Most PLC algorithms were developed for speech codecs with time-domain predictive coding [1][2][3][4]. They concentrate attentions on digital speech transmission and work well for speech audio signals but yield poor results for audio signals with music.…”

Section: Introductionmentioning

confidence: 99%

An effective hybrid low delay packet loss concealment algorithm for MDCT-based audio codec

2019

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Fusion Feature Extraction Based on Auditory and Energy for Noise-Robust Speech Recognition

et al. 2019

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Environmental noise can pose a threat to the stable operation of current speech recognition systems. It is therefore essential to develop a front feature set that is able to identify speech under low signalto-noise ratio. In this paper, a robust fusion feature is proposed that can fully characterize speech information. To obtain the cochlear filter cepstral coefficients (CFCC), a novel feature is first extracted by the power-law nonlinear function, which can simulate the auditory characteristics of the human ear. Speech enhancement technology is then introduced into the front end of feature extraction, and the extracted feature and their first-order difference are combined in new mixed features. An energy feature Teager energy operator cepstral coefficient (TEOCC) is also extracted, and combined with the above-mentioned mixed features to form the fusion feature sets. Principal component analysis (PCA) is then applied to feature selection and optimization of the feature set, and the final feature set is used in a non-specific persons, isolated words, and smallvocabulary speech recognition system. Finally, a comparative experiment of speech recognition is designed to verify the advantages of the proposed feature set using a support vector machine (SVM). The experimental results show that the proposed feature set not only display a high recognition rate and excellent anti-noise performance in speech recognition, but can also fully characterize the auditory and energy information in the speech signals.INDEX TERMS Cochlear filter cepstral coefficients, Teager energy operators cepstral coefficients, principal component analysis, speech recognition.

show abstract

Speech excitation signal recovering based on a novel error mitigation scheme under erasure channel conditions

Cited by 4 publications

References 27 publications

Combining Evidence from Auditory, Instantaneous Frequency and Random Forest for Anti-Noise Speech Recognition

Combining Evidence from Auditory, Instantaneous Frequency and Random Forest for Anti-Noise Speech Recognition

An effective hybrid low delay packet loss concealment algorithm for MDCT-based audio codec

Fusion Feature Extraction Based on Auditory and Energy for Noise-Robust Speech Recognition

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