Adaptive feature truncation to address acoustic mismatch in automatic recognition of children's speech

Ghai, Shweta; Sinha, Rohit

doi:10.1017/atsip.2016.16

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…However, the quality of the recordings is not necessarily as expected, as they might be subject to degradation. In practice, the presence of degradation during the operating time can deteriorate the performance of speech-based systems, such as speech recognition [1], speaker identification [2], and pathological voice analysis (assessment of voice signal of a speaker with a voice disorder) [3,4], mainly due to acoustic mismatch between training and operating conditions. The Page 2 of 10 more complex.…”

Section: Introductionmentioning

confidence: 99%

A CNN-based approach to identification of degradations in speech signals

Saishu

Poorjam

Christensen

2021

J AUDIO SPEECH MUSIC PROC.

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The presence of degradations in speech signals, which causes acoustic mismatch between training and operating conditions, deteriorates the performance of many speech-based systems. A variety of enhancement techniques have been developed to compensate the acoustic mismatch in speech-based applications. To apply these signal enhancement techniques, however, it is necessary to know prior information about the presence and the type of degradations in speech signals. In this paper, we propose a new convolutional neural network (CNN)-based approach to automatically identify the major types of degradations commonly encountered in speech-based applications, namely additive noise, nonlinear distortion, and reverberation. In this approach, a set of parallel CNNs, each detecting a certain degradation type, is applied to the log-mel spectrogram of audio signals. Experimental results using two different speech types, namely pathological voice and normal running speech, show the effectiveness of the proposed method in detecting the presence and the type of degradations in speech signals which outperforms the state-of-the-art method. Using the score weighted class activation mapping, we provide a visual analysis of how the network makes decision for identifying different types of degradation in speech signals by highlighting the regions of the log-mel spectrogram which are more influential to the target degradation.

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Section: Introductionmentioning

confidence: 99%

A CNN-based approach to identification of degradations in speech signals

Saishu

Poorjam

Christensen

2021

J AUDIO SPEECH MUSIC PROC.

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Improving End-to-End Models for Children’s Speech Recognition

Patel,

Scharenborg

2024

Applied Sciences

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Children’s Speech Recognition (CSR) is a challenging task due to the high variability in children’s speech patterns and limited amount of available annotated children’s speech data. We aim to improve CSR in the often-occurring scenario that no children’s speech data is available for training the Automatic Speech Recognition (ASR) systems. Traditionally, Vocal Tract Length Normalization (VTLN) has been widely used in hybrid ASR systems to address acoustic mismatch and variability in children’s speech when training models on adults’ speech. Meanwhile, End-to-End (E2E) systems often use data augmentation methods to create child-like speech from adults’ speech. For adult speech-trained ASRs, we investigate the effectiveness of augmentation methods; speed perturbations and spectral augmentation, along with VTLN, in an E2E framework for the CSR task, comparing these across Dutch, German, and Mandarin. We applied VTLN at different stages (training/test) of the ASR and conducted age and gender analyses. Our experiments showed highly similar patterns across the languages: Speed Perturbations and Spectral Augmentation yield significant performance improvements, while VTLN provided further improvements while maintaining recognition performance on adults’ speech (depending on when it is applied). Additionally, VTLN showed performance improvement for both male and female speakers and was particularly effective for younger children.

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Adaptive feature truncation to address acoustic mismatch in automatic recognition of children's speech

Cited by 2 publications

References 22 publications

A CNN-based approach to identification of degradations in speech signals

A CNN-based approach to identification of degradations in speech signals

Improving End-to-End Models for Children’s Speech Recognition

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