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008			211005s2022 sz | s |||| 0|eng d
020			_a9783030807412
024	7		_a10.1007/978-3-030-80741-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK7895.S65 _b2022 EB
100	1		_aManjunath, K.E. _eautor _9687121
245	1	0	_aMultilingual Phone Recognition in Indian Languages _cby K.E Manjunath
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XIV, 103 páginas) _b28 ilustraciones, 9 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSpringerBriefs in Speech Technology Studies in Speech Signal Processing Natural Language Understanding and Machine Learning _x2191-7388
505	0		_a1. Introduction -- 2. Literature review -- 3. Development and analysis of Multilingual Phone recognition system -- 4. Prediction of Multilingual Articulatory Features -- 5. Articulatory Features of Multilingual Phone recognition -- 6. Applications of Multilingual Phone recognition in Code-switched and Non-code-switched Scenarios -- 7. Summary and Conclusion.
520			_aThe book presents current research and developments in multilingual speech recognition. The author presents a Multilingual Phone Recognition System (Multi-PRS), developed using a common multilingual phone-set derived from the International Phonetic Alphabets (IPA) based transcription of six Indian languages - Kannada, Telugu, Bengali, Odia, Urdu, and Assamese. The author shows how the performance of Multi-PRS can be improved using tandem features. The book compares Monolingual Phone Recognition Systems (Mono-PRS) versus Multi-PRS and baseline versus tandem system. Methods are proposed to predict Articulatory Features (AFs) from spectral features using Deep Neural Networks (DNN). Multitask learning is explored to improve the prediction accuracy of AFs. Then, the AFs are explored to improve the performance of Multi-PRS using lattice rescoring method of combination and tandem method of combination. The author goes on to develop and evaluate the Language Identification followed by Monolingual phone recognition (LID-Mono) and common multilingual phone-set based multilingual phone recognition systems.
988			_aSpringer_Engineering_2022
650		7	_2embne _9147323 _aReconocimiento automático del lenguaje
776	0	8	_iPrinted edition: _z9783030807405
776	0	8	_iPrinted edition: _z9783030807429
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-80741-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _eu _zSI