Investigating Noise Interference on Speech Towards Applying the Lombard Effect Automatically

Korvel, Gražina; Kąkol, Krzysztof; Treigys, Povilas; Kostek, Bożena

doi:10.1007/978-3-031-16564-1_38

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…The investigations conducted also include an initial attempt to modify neutral speech samples based on noise profiling and test them using a speech quality indicator. Automatic noise profiling, utilizing the developed noise profiling method presented initially in a paper by Korvel et al (Korvel et al, 2022) and later extended and published by Kąkol et al (Kąkol et al, 2022), is used for this purpose. This method employs machine learning to perform near-real-time noise profiling.…”

Section: Resultsmentioning

confidence: 99%

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Lombard effect – is it a remedy for speech communication in noisy environments?

Korvel

Kąkol

Treigys

et al. 2023

Preprint

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The aim of this study is to address the question of whether the Lombard effect, either natural (an involuntary tendency to raise the uttered speech level in the presence of background noise) or synthetically created, is a remedy for speech communication in noise. To this end, a series of experiments to examine the interference of different noises in synthesizing the Lombard effect is performed. Several steps are proposed; first, a recording session is carried out to prepare a dataset of speech with and without the Lombard effect in a controlled environment. Then, we detect frequency changes at each time point on the 2D speech representation. Determining frequency tracks in a speech signal is performed using McAulay and Quartieri algorithm. To quantify the effect of noise on speech, containing the Lombard effect, an average formant track error is calculated as an objective image quality metric. Three image assessment measures, i.e., SSIM (Structural SIMilarity) index, RMSE (Root Mean Square Error), and dHash (Difference Hash), are employed for that purpose. Moreover, several spectral descriptors are analyzed in the context of Lombard speech and various types of noise to discuss their influence on speech. The investigations are concluded with an initial attempt at automatic noise profiling based on the method developed, followed by pitch modifications of neutral speech signal depending on the profiling result and frequency change trends obtained. An overlap-add synthesis in the STRAIGHT vocoder is used for synthesized speech.

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Section: Resultsmentioning

confidence: 99%

“…This article is an extended version of the paper presented by the authors at the ISMIS conference (Korvel et al, 2022). This version differs in several aspects from its initial version.…”

Section: Introductionmentioning

confidence: 99%

Lombard effect – is it a remedy for speech communication in noisy environments?

Korvel

Kąkol

Treigys

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The investigations conducted also included an attempt to modify neutral speech samples based on noise profiling and test them using a speech-quality indicator. Automatic noise profiling, utilizing the developed noise profiling method presented initially in a paper by Korvel et al [19], and later extended and published by Kąkol et al [41], was used for this purpose. This method employs machine learning to perform near-real-time In the case of the RMSE metric, many high standard deviation values were obtained.…”

Section: Modification Of the Neutral Speech Samples Based On Noise Pr...mentioning

confidence: 99%

Applying the Lombard Effect to Speech-in-Noise Communication

Korvel,

Kąkol,

Treigys

et al. 2023

Electronics

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This study explored how the Lombard effect, a natural or artificial increase in speech loudness in noisy environments, can improve speech-in-noise communication. This study consisted of several experiments that measured the impact of different types of noise on synthesizing the Lombard effect. The main steps were as follows: first, a dataset of speech samples with and without the Lombard effect was collected in a controlled setting; then, the frequency changes in the speech signals were detected using the McAulay and Quartieri algorithm based on a 2D speech representation; next, an average formant track error was computed as a metric to evaluate the quality of the speech signals in noise. Three image assessment methods, namely the SSIM (Structural SIMilarity) index, RMSE (Root Mean Square Error), and dHash (Difference Hash) were used for this purpose. Furthermore, this study analyzed various spectral features of the speech signals in relation to the Lombard effect and the noise types. Finally, this study proposed a method for automatic noise profiling and applied pitch modifications to neutral speech signals according to the profile and the frequency change patterns. This study used an overlap-add synthesis in the STRAIGHT vocoder to generate the synthesized speech.

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Investigating Noise Interference on Speech Towards Applying the Lombard Effect Automatically

Cited by 2 publications

References 16 publications

Lombard effect – is it a remedy for speech communication in noisy environments?

Lombard effect – is it a remedy for speech communication in noisy environments?

Applying the Lombard Effect to Speech-in-Noise Communication

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