Gender Identification From Children's Speech

Ramteke, Pravin Bhaskar; Dixit, Amulya A.; Supanekar, Sujata; Dharwadkar, Nagaraj V.; Koolagudi, Shashidhar G.

doi:10.1109/ic3.2018.8530666

Cited by 2 publications

(5 citation statements)

References 16 publications

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“…In addition to using the logistic regression as a classifier. [7] The OGI Kids Corpus 57 79.18 Ramteke et al [5] The CMU Kids Corpus 68 84.79 The proposed system…”

Section: Resultsmentioning

confidence: 99%

“…Their experiments have been conducted on the OGI Kids corpus; their best result was a 79.18% identification rate. Ramteke et al [5] presented an attempt for exploring the features effective in differentiating the gender from the speech of children. Also, they examined various spectral features' combinations, like mel-frequency cepstral coefficients (MFCCs) as well as its 1st and 2 nd derivate, linear predictive cepstral coefficients (LPCCs), formants, jitter, shimmer and prosodic features like pitch and its statistical variations.…”

Section: Related Workmentioning

confidence: 99%

“…The formant frequencies' values decrease as the vocal tract length increases. Both male and female adults have higher formant frequencies compared to children [5], [13], [14]. Formants were only measured at the glottal pulse to make the measurement easier along with the whole utterance.…”

Section: Features Extraction Based On Formantsmentioning

confidence: 99%

“…Thus, that difficult phoneme is replaced by those children with another simpler phoneme. These replacement patterns are referred to as phonological processes [5]. Children are enthusiastic adopters of new technology.…”

Section: Introductionmentioning

confidence: 99%

“…5, October 2022: 5049-5054 5050 purposes. In telecommunication and speech therapy, determining the gender of a child speaker based on his or her speech is critical [5], [6]. Children's speech has distinct linguistic and acoustic characteristics from that of adults.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Gender detection in children’s speech utterances for human-robot interaction

Badr

Hassan²

2022

IJECE

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The human voice speech essentially includes paralinguistic information used in many real-time applications. Detecting the children’s gender is considered a challenging task compared to the adult’s gender. In this study, a system for human-robot interaction (HRI) is proposed to detect the gender in children’s speech utterances without depending on the text. The robot's perception includes three phases: Feature’s extraction phase where four formants are measured at each glottal pulse and then a median is calculated across these measurements. After that, three types of features are measured which are formant average (AF), formant dispersion (DF), and formant position (PF). Feature’s standardization phase where the measured feature dimensions are standardized using the z-score method. The semantic understanding phase is where the children’s gender is detected accurately using the logistic regression classifier. At the same time, the action of the robot is specified via a speech response using the text to speech (TTS) technique. Experiments are conducted on the Carnegie Mellon University (CMU) Kids dataset to measure the suggested system’s performance. In the suggested system, the overall accuracy is 98%. The results show a relatively clear improvement in terms of accuracy of up to 13% compared to related works that utilized the CMU Kids dataset.

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“…In addition to using the logistic regression as a classifier. [7] The OGI Kids Corpus 57 79.18 Ramteke et al [5] The CMU Kids Corpus 68 84.79 The proposed system…”

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%