Auditory Processing Inspired Robust Feature Enhancement for Speech Recognition

Maganti, Hari Krishna; Matassoni, Marco

doi:10.1007/978-3-642-29752-6_15

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…In this work, we adopt gammatonegrams, which are a visual representation of the energy of a signal based on short-time Fourier transform (STFT) and the application of Gammatone filterbanks [13], which have been firstly introduced in [11]. It has been proved, indeed, that such filtering, is able to guarantee robustness to noise for speech analysis tasks [15,24]. The gammatonegrams are generated following the specification illustrated in [29] and [17], employing a bank of 64 filters.…”

Section: Feature Representationmentioning

confidence: 99%

No Need to Scream: Robust Sound-Based Speaker Localisation in Challenging Scenarios

2019

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This paper is about speaker verification and horizontal localisation in the presence of conspicuous noise. Specifically, we are interested in enabling a mobile robot to robustly and accurately spot the presence of a target speaker and estimate his/her position in challenging acoustic scenarios. While several solutions to both tasks have been proposed in the literature, little attention has been devoted to the development of systems able to function in harsh noisy conditions. To address these shortcomings, in this work we follow a purely data-driven approach based on deep learning architectures which, by not requiring any knowledge either on the nature of the masking noise or on the structure and acoustics of the operation environment, it is able to reliably act in previously unexplored acoustic scenes. Our experimental evaluation, relying on data collected in real environments with a robotic platform, demonstrates that our framework is able to achieve high performance both in the verification and localisation tasks, despite the presence of copious noise.

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Section: Feature Representationmentioning

confidence: 99%

No Need to Scream: Robust Sound-Based Speaker Localisation in Challenging Scenarios

2019

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“…Gammatonegrams decompose a signal by passing it through a bank of gammatone filters equally spaced on the ERB scale and were designed to model the human auditory system. We opted for gammatonegrams as they have proven to be robust to noisy environments [28] and thus suited to deal with robot data. We pre-processed the Dataset voices by taking chunks of audio of 1 second with a hop-length of 250ms and we discarded chunks having less than 80% of voice data using the Google voice activity detector.…”

Section: Multi Classificationmentioning

confidence: 99%

Cognitive architecture aided by working-memory for self-supervised multi-modal humans recognition

Gonzalez-Billandon¹,

Belgiovine²,

Sciutti³

et al. 2021

Preprint

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The ability to recognize human partners is an important social skill to build personalized and long-term human-robot interactions, especially in scenarios like education, care-giving, and rehabilitation. Faces and voices constitute two important sources of information to enable artificial systems to reliably recognize individuals. Deep learning networks have achieved state-of-the-art results and demonstrated to be suitable tools to address such a task. However, when those networks are applied to different and unprecedented scenarios not included in the training set, they can suffer a drop in performance. For example, with robotic platforms in ever-changing and realistic environments, where always new sensory evidence is acquired, the performance of those models degrades. One solution is to make robots learn from their first-hand sensory data with selfsupervision. This allows coping with the inherent variability of the data gathered in realistic and interactive contexts. To this aim, we propose a cognitive architecture integrating low-level perceptual processes with a spatial working memory mechanism. The architecture autonomously organizes the robot's sensory experience into a structured dataset suitable for human recognition. Our results demonstrate the effectiveness of our architecture and show that it is a promising solution in the quest of making robots more autonomous in their learning process.

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Auditory Processing Inspired Robust Feature Enhancement for Speech Recognition

Cited by 2 publications

References 24 publications

No Need to Scream: Robust Sound-Based Speaker Localisation in Challenging Scenarios

No Need to Scream: Robust Sound-Based Speaker Localisation in Challenging Scenarios

Cognitive architecture aided by working-memory for self-supervised multi-modal humans recognition

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