A study of speech distortion conditions in real scenarios for speech processing applications

Ribas, Dayana; Calvo, José R.

doi:10.1109/slt.2016.7846239

Cited by 5 publications

(6 citation statements)

References 43 publications

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“…The residual mechanism was extremely useful in this case, since the signal has always a linear shortcut and the non-linear path enhances it in certain steps by adding or subtracting corrections. In practical applications, this is a valuable property because realistic scenarios could challenge the system with many different conditions, and sometimes the real world is not so noisy as research studies consider in experimental setups [63].…”

Section: Discussionmentioning

confidence: 99%

Deep Speech Enhancement for Reverberated and Noisy Signals using Wide Residual Networks

Ribas,

Llombart,

Miguel

et al. 2019

Preprint

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This paper proposes a deep speech enhancement method which exploits the high potential of residual connections in a wide neural network architecture, a topology known as Wide Residual Network. This is supported on single dimensional convolutions computed alongside the time domain, which is a powerful approach to process contextually correlated representations through the temporal domain, such as speech feature sequences. We find the residual mechanism extremely useful for the enhancement task since the signal always has a linear shortcut and the non-linear path enhances it in several steps by adding or subtracting corrections. The enhancement capacity of the proposal is assessed by objective quality metrics and the performance of a speech recognition system. This was evaluated in the framework of the REVERB Challenge dataset, including simulated and real samples of reverberated and noisy speech signals. Results showed that enhanced speech from the proposed method succeeded for both, the enhancement task with intelligibility purposes and the speech recognition system. The DNN model, trained with artificial synthesized reverberation data, was able to deal with far-field reverberated speech from real scenarios. Furthermore, the method was able to take advantage of the residual connection achieving to enhance signals with low noise level, which is usually a strong handicap of traditional enhancement methods.

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Section: Discussionmentioning

confidence: 99%

Deep Speech Enhancement for Reverberated and Noisy Signals using Wide Residual Networks

Ribas,

Llombart,

Miguel

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The residual mechanism was extremely useful in this case since the signal has always a linear shortcut and the non-linear path enhances it in certain steps by adding or subtracting corrections. In practical applications, this is a valuable property because realistic scenarios could challenge the system with many different conditions [28].…”

Section: Discussionmentioning

confidence: 99%

Speech Enhancement with Wide Residual Networks in Reverberant Environments

et al. 2019

Self Cite

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This paper proposes a speech enhancement method which exploits the high potential of residual connections in a Wide Residual Network architecture. This is supported on single dimensional convolutions computed alongside the time domain, which is a powerful approach to process contextually correlated representations through the temporal domain, such as speech feature sequences. We find the residual mechanism extremely useful for the enhancement task since the signal always has a linear shortcut and the non-linear path enhances it in several steps by adding or subtracting corrections. The enhancement capability of the proposal is assessed by objective quality metrics evaluated with simulated and real samples of reverberated speech signals. Results show that the proposal outperforms the state-of-the-art method called WPE, which is known to effectively reduce reverberation and greatly enhance the signal. The proposed model, trained with artificial synthesized reverberation data, was able to generalize to real room impulse responses for a variety of conditions (e.g. different room sizes, RT60, near & far field). Furthermore, it achieves accuracy for real speech with reverberation from two different datasets.

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“…Finally, the speech mixtures were reverberated using RIRs ( 26 ) dataset to simulate different room settings (e.g., small room, medium room, and large room). Based on the studies ( 27 , 28 ) that conducted comprehensive analyses of daily acoustic scenarios in terms of noise and reverberation level, our synthetic datasets were able to simulate various scenarios like bedroom, kitchen, meeting room, office, classroom, restaurant, hospital hall, etc., as shown in Figure 3 .…”

Section: Methodsmentioning

confidence: 99%

“… Simulated scenarios in our synthetic datasets, based on the comprehensive analyses of daily acoustic scenarios in terms of noise and reverberation level ( 27 , 28 ). …”

Section: Methodsmentioning

confidence: 99%

Privacy-Preserving Social Ambiance Measure From Free-Living Speech Associates With Chronic Depressive and Psychotic Disorders

et al. 2021

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A social interaction consists of contributions by the individual, the environment and the interaction between the two. Ideally, to enable effective assessment and interventions for social isolation, an issue inherent to depressive and psychotic illnesses, the isolation must be identified in real-time and at an individual level. However, research addressing sociability deficits is largely focused on determining loneliness, rather than isolation, and lacks focus on the richness of the social environment the individual revolves in. In this paper, We describe the development of an automated, objective and privacy-preserving Social Ambiance Measure (SAM) that converts unconstrained audio recordings collected from wrist-worn audio-bands into four levels, ranging from none to active. The ambiance levels are based on the number of simultaneous speakers, which is a proxy for overall social activity in the environment. Results show that social ambiance patterns and time spent at each ambiance level differed between participants with depressive or psychotic disorders and healthy controls. Individuals with depression/psychosis spent less time in diverse environments and less time in moderate/active ambiance levels. Moreover, social ambiance patterns are found associated with the severity of self-reported depression, anxiety symptoms and personality traits. The results in this paper suggest that objectively measured social ambiance can be used as a marker of sociability, and holds potential to be leveraged to better understand social isolation and develop effective interventions for sociability challenges, thus improving mental health outcomes.

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A study of speech distortion conditions in real scenarios for speech processing applications

Cited by 5 publications

References 43 publications

Deep Speech Enhancement for Reverberated and Noisy Signals using Wide Residual Networks

Deep Speech Enhancement for Reverberated and Noisy Signals using Wide Residual Networks

Speech Enhancement with Wide Residual Networks in Reverberant Environments

Privacy-Preserving Social Ambiance Measure From Free-Living Speech Associates With Chronic Depressive and Psychotic Disorders

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