A survey about databases of children's speech

Claus, Felix; Gamboa-Rosales, Hamurabi; Petrick, Rico; Hain, Horst-Udo; Hoffmann, Rüdiger

doi:10.21437/interspeech.2013-561

Cited by 21 publications

(9 citation statements)

References 11 publications

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“…7 However, it is worth pointing out that UGC data also has the potential to aid in automatic speech recognition (ASR) in arbitrarily noisy environments, especially in recognizing children's speech. Again because of the additional necessary permissions and precautions, ASR researchers have a much smaller pool of recorded corpus data to draw from for recognizing the speech of children, in comparison to the available resources for adult speech [31]. 8 We use emotion (expression) and affect somewhat interchangeably in talking about the whole field; for particular applications, researchers often draw finer distinctions.…”

Section: Interaction Training For Ai Systemsmentioning

confidence: 99%

Field Studies with Multimedia Big Data: Opportunities and Challenges (Extended Version)

Krell¹,

Bernd²,

Li³

et al. 2017

Preprint

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Social multimedia users are increasingly sharing all kinds of data about the world. They do this for their own reasons, not to provide data for field studies-but the trend presents a great opportunity for scientists. The Yahoo Flickr Creative Commons 100 Million (YFCC100M) dataset comprises 99 million images and nearly 800 thousand videos from Flickr, all shared under Creative Commons licenses. To enable scientists to leverage these media records for field studies, we propose a new framework that extracts targeted subcorpora from the YFCC100M, in a format usable by researchers who are not experts in big data retrieval and processing.

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Section: Interaction Training For Ai Systemsmentioning

confidence: 99%

Field Studies with Multimedia Big Data: Opportunities and Challenges (Extended Version)

Krell¹,

Bernd²,

Li³

et al. 2017

Preprint

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“…The majority of commercial ASR systems available for adult speech are running successfully (2,3). Children's speech recognition systems become more important to develop due to their applications solely for children, such as reading tutors, foreign language learning tools, clinical applications, and toys & computer games (2)(3)(4). Computerized learning aid having a speech interface is beneficial for younger children who are yet to develop deftness to use the conventional input interfaces such as a keyboard, mouse, and pointer (2).…”

Section: Introductionmentioning

confidence: 99%

Challenges for Designing of Children Speech Corpora: A State-of-the-Art Review

Sobti¹,

Kadyan²,

Guleria³

2022

ECS Trans.

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Automatic Speech Recognition (ASR) is the use of computer hardware and software-based techniques to identify and process human voices. These systems used data from both male and female speakers. The majority of commercial ASR systems available on adult speech are working efficiently. Speech data collected from both male and female speakers were used in these systems. In recent decades, ASR systems for children have emerged, such as reading tutors, aids for foreign language learning, and computer games. Child ASR systems are essential but poorly understood in the field of computer speech recognition. The child data collection is a very complex task. Child corpus is not available publicly, and variability of children speakers and ASR developed for a particular age group is not suitable for other age groups. These are some of the reasons for less and ineffective child ASR systems. However, the non-availability of child corpus publicly is a primary reason for ineffective child ASR. Designing and developing child corpus is a very tedious task. Therefore, the primary focus of this state-of-the-art review is to discuss various challenges encountered while designing and developing child corpus.

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“…However, the performance of such systems for children's speech suffers from the large inter-speaker variability due to differing rates of growth, and intra-speaker variability due to undeveloped pronunciation skills, especially at very young ages (3-12 years) [1]. Furthermore, the lack of adequate and appropriate children speech resources add to the challenge of designing a robust ASR system [2]. Improving children's speech recognition has substantial implications due to the growing number of applications in human-computer interaction, online education, computer-assisted language learning, speech-enabled smart toys, etc.…”

Section: Introductionmentioning

confidence: 99%

Spectral Modification Based Data Augmentation For Improving End-to-End ASR For Children's Speech

Singh¹,

Sailor²,

Bhattacharya³

et al. 2022

Preprint

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Training a robust Automatic Speech Recognition (ASR) system for children's speech recognition is a challenging task due to inherent differences in acoustic attributes of adult and child speech and scarcity of publicly available children's speech dataset. In this paper, a novel segmental spectrum warping and perturbations in formant energy are introduced, to generate a children-like speech spectrum from that of an adult's speech spectrum. Then, this modified adult spectrum is used as augmented data to improve end-to-end ASR systems for children's speech recognition. The proposed data augmentation methods give 6.5% and 6.1% relative reduction in WER on children dev and test sets respectively, compared to the vocal tract length perturbation (VTLP) baseline system trained on Librispeech 100 hours adult speech dataset. When children's speech data is added in training with Librispeech set, it gives a 3.7 % and 5.1% relative reduction in WER, compared to the VTLP baseline system.

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A survey about databases of children's speech

Cited by 21 publications

References 11 publications

Field Studies with Multimedia Big Data: Opportunities and Challenges (Extended Version)

Field Studies with Multimedia Big Data: Opportunities and Challenges (Extended Version)

Challenges for Designing of Children Speech Corpora: A State-of-the-Art Review

Spectral Modification Based Data Augmentation For Improving End-to-End ASR For Children's Speech

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