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PROBLEMS OF THE IMPLEMENTATION OF THE BASIC SOCIAL FUNCTIONS OF THE LANGUAGE ...
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PROBLEMS OF THE IMPLEMENTATION OF THE BASIC SOCIAL FUNCTIONS OF THE LANGUAGE ...
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Towards a part-of-speech tagger for Sranan Tongo ...
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Nicolás, C.V.; Viktor, Z.. - : Фонд содействия развитию интернет-медиа, ИТ-образования, человеческого потенциала "Лига интернет-медиа", 2022
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Potential of automatic speech processing technologies for early detection of oral language disorders: a meta-analytic review ...
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A comparative study of several parameterizations for speaker recognition ...
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Speaker verification in mismatch training and testing conditions ...
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Speech Segmentation Optimization using Segmented Bilingual Speech Corpus for End-to-end Speech Translation ...
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РЕЧЬ ПРЕПОДАВАТЕЛЯ КАК МОТИВАЦИЯ К ИЗУЧЕНИЮ НЕРОДНОГО ЯЗЫКА ... : THE TEACHER'S SPEECH AS A MOTIVATION TO LEARN NON NATIVE LANGUAGE ...
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Estimating Vocal Tract Resonances of Synthesized High-Pitched Vowels Using CNN ...
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Dvoice : An open source dataset for Automatic Speech Recognition on African Languages and Dialects ...
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Dvoice : An open source dataset for Automatic Speech Recognition on African Languages and Dialects ...
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A New Amharic Speech Emotion Dataset and Classification Benchmark ...
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Lahjoita puhetta -- a large-scale corpus of spoken Finnish with some benchmarks ...
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The Norwegian Parliamentary Speech Corpus ...
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Abstract:
The Norwegian Parliamentary Speech Corpus (NPSC) is a speech dataset with recordings of meetings from Stortinget, the Norwegian parliament. It is the first, publicly available dataset containing unscripted, Norwegian speech designed for training of automatic speech recognition (ASR) systems. The recordings are manually transcribed and annotated with language codes and speakers, and there are detailed metadata about the speakers. The transcriptions exist in both normalized and non-normalized form, and non-standardized words are explicitly marked and annotated with standardized equivalents. To test the usefulness of this dataset, we have compared an ASR system trained on the NPSC with a baseline system trained on only manuscript-read speech. These systems were tested on an independent dataset containing spontaneous, dialectal speech. The NPSC-trained system performed significantly better, with a 22.9% relative improvement in word error rate (WER). Moreover, training on the NPSC is shown to have a ... : 6 pages, submitted to LREC 2022 ...
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Keyword:
Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Sound cs.SD
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URL: https://dx.doi.org/10.48550/arxiv.2201.10881 https://arxiv.org/abs/2201.10881
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Subspace-based Representation and Learning for Phonotactic Spoken Language Recognition ...
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LPC Augment: An LPC-Based ASR Data Augmentation Algorithm for Low and Zero-Resource Children's Dialects ...
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Automatic Dialect Density Estimation for African American English ...
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