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RETRIEVING SPEAKER INFORMATION FROM PERSONALIZED ACOUSTIC MODELS FOR SPEECH RECOGNITION
In: IEEE ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03539741 ; IEEE ICASSP 2022, 2022, Singapour, Singapore (2022)
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Hippocampal and auditory contributions to speech segmentation
In: ISSN: 0010-9452 ; Cortex ; https://hal.archives-ouvertes.fr/hal-03604957 ; Cortex, Elsevier, 2022, ⟨10.1016/j.cortex.2022.01.017⟩ (2022)
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Cross-lingual few-shot hate speech and offensive language detection using meta learning
In: ISSN: 2169-3536 ; EISSN: 2169-3536 ; IEEE Access ; https://hal.archives-ouvertes.fr/hal-03559484 ; IEEE Access, IEEE, 2022, 10, pp.14880-14896. ⟨10.1109/ACCESS.2022.3147588⟩ (2022)
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МОНОЛОГИЧЕСКАЯ РЕЧЬ С ТОЧКИ ЗРЕНИЯ УЧЁНЫХ ... : MONOLOGICAL SPEECH FROM THE POINT OF VIEW OF SCIENTISTS ...
Н. И. Шадманова. - : Academic research in educational sciences, 2022
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5
A comparative study of several parameterizations for speaker recognition ...
Faundez-Zanuy, Marcos. - : arXiv, 2022
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6
Subspace-based Representation and Learning for Phonotactic Spoken Language Recognition ...
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7
REYD Yiddish TTS Corpus ...
Unkn Unknown. - : Centre for Speech Technology Research (CSTR), 2022
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8
Data From: A Protracted Developmental Trajectory for English-Learning Children’s Detection of Consonant Mispronunciations in Newly Learned Words
In: Speech and Hearing Sciences Faculty Datasets (2022)
Abstract: Children are adept at learning their language’s speech-sound categories, but just how these categories function in their developing lexicon has not been mapped out in detail. Here, we addressed whether, in a language-guided looking procedure, two-year-olds would respond to a mispronunciation of the voicing of the initial consonant of a newly learned word. First, to provide a baseline of mature native-speaker performance, adults were taught a new word under training conditions of low prosodic variability. In a second experiment, 24- and 30-month-olds were taught a new word under training conditions of high or low prosodic variability. Children and adults showed evidence of learning the taught word. Adults’ target looking was reduced when the novel word was realized at test with a change in the voicing of the initial consonant, but children did not show any such decrement in target fixation. For both children and adults, most learners did not treat the phonologically distinct variant as a different word. Phonetic variability during teaching did not have consistent effects. Thus, under conditions of intensive short-term training, 24- and 30-month-olds did not differentiate a newly learned word from a variant differing only in consonant voicing. High task complexity during training could explain why mispronunciation detection was weaker here than in some prior studies. We also tested 19-month-olds in the low-variability condition, because we originally predicted that children would learn words and detect one-feature mispronunciations as early as 19 months. However, 19-month-olds showed inconsistent word learning, suggesting that the narrated story we used for word teaching might have been too complex. Thus, in the article we focus on the work with 24- and 30-month-olds, presenting the 19-month-olds’ results in the Supplemental Materials. Their data are included in the data file in the interests of transparency. The Supplemental Materials accompanying the paper also report analyses of (lack of) responses to pitch mispronunciations by adults and 19-month-olds. Adults were tested with both pitch and consonant MPs. Nineteen-month-olds were tested with one or the other, due to more limited attention spans.
Keyword: Phonology; Processing; Prosody; Speech and Hearing Science; Speech Pathology and Audiology; Word learning
URL: https://pdxscholar.library.pdx.edu/sphr_data/1
https://pdxscholar.library.pdx.edu/context/sphr_data/article/1000/type/native/viewcontent
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9
Intoxication and pitch control in tonal and non-tonal language speakers ...
Chang, Charles. - : Open Science Framework, 2022
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10
Materials for Tang et al. (2022) ...
Chang, Charles. - : Open Science Framework, 2022
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11
Dataset for Tang et al. (2022) ...
Chang, Charles. - : Open Science Framework, 2022
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12
COLORINCO ...
, Caudrelier. - : Open Science Framework, 2022
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13
Learning and controlling the source-filter representation of speech with a variational autoencoder ...
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14
Correcting Misproducted Speech using Spectrogram Inpainting ...
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15
The 2021 NIST Speaker Recognition Evaluation ...
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16
Multilingual and Multimodal Abuse Detection ...
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17
WavThruVec: Latent speech representation as intermediate features for neural speech synthesis ...
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18
CTA-RNN: Channel and Temporal-wise Attention RNN Leveraging Pre-trained ASR Embeddings for Speech Emotion Recognition ...
Chen, Chengxin; Zhang, Pengyuan. - : arXiv, 2022
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19
Fine-grained Noise Control for Multispeaker Speech Synthesis ...
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20
Emotion Intensity and its Control for Emotional Voice Conversion ...
Zhou, Kun; Sisman, Berrak; Rana, Rajib. - : arXiv, 2022
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