The SIWIS Database: A Multilingual Speech Database with Acted Emphasis

We describe here a collection of speech data of bilingual and trilingual speakers of English, French, German and Italian. In the context of speech to speech translation (S2ST), this database is designed for several purposes and studies: training CLSA systems (cross-language speaker adaptation), conveying emphasis through S2ST systems, and evaluating TTS systems. More precisely, 36 speakers judged as accentless (22 bilingual and 14 trilingual speakers) were recorded for a set of 171 prompts in two or three languages, amounting to a total of 24 hours of speech. These sets of prompts include 100 sentences from news, 25 sentences from Europarl, the same 25 sentences with one acted emphasised word, 20 semantically unpredictable sentences, and finally a 240-word long text. All in all, it yielded 64 bilingual session pairs of the six possible combinations of the four languages. The database is freely available for non-commercial use and scientific research purposes

A Systematic Literature Review of Hausa Natural Language Processing

The processing of natural languages is an area of computer science that has gained growing attention recently. NLP helps computers recognize, in other words, the ways in which people use their language. NLP research, however, has been performed predominantly on languages with abundant quantities of annotated data, such as English, French, German and Arabic. While the Hausa Language is Africa's second most commonly used language, only a few studies have so far focused on Hausa Natural Language Processing (HNLP). In this research paper, using a keyword index and article title search, we present a systematic analysis of the current literature applicable to HNLP in the Google Scholar database from 2015 to June 2020. A very few research papers on HNLP research, especially in areas such as part-of-speech tagging (POS), Name Entity Recognition (NER), Words Embedding, Speech Recognition and Machine Translation, have just recently been released. This is due to the fact that for training intelligent models, NLP depends on a huge amount of human-annotated data. HNLP is now attracting researchers' attention after extensive research on NLP in English and other languages has been performed. The key objectives of this paper are to promote research, to define likely areas for future studies in the HNLP, and to assist in the creation of further examinations by researchers for relevant studies

Statistical text-to-speech synthesis of Spanish subtitles

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-13623-3_5Online multimedia repositories are growing rapidly. However, language barriers are often difficult to overcome for many of the current and potential users. In this paper we describe a TTS Spanish sys- tem and we apply it to the synthesis of transcribed and translated video lectures. A statistical parametric speech synthesis system, in which the acoustic mapping is performed with either HMM-based or DNN-based acoustic models, has been developed. To the best of our knowledge, this is the first time that a DNN-based TTS system has been implemented for the synthesis of Spanish. A comparative objective evaluation between both models has been carried out. Our results show that DNN-based systems can reconstruct speech waveforms more accurately.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no 287755 (transLectures) and ICT Policy Support Programme (ICT PSP/2007-2013) as part of the Competitiveness and Innovation Framework Programme (CIP) under grant agreement no 621030 (EMMA), and the Spanish MINECO Active2Trans (TIN2012-31723) research project.Piqueras Gozalbes, SR.; Del Agua Teba, MA.; Giménez Pastor, A.; Civera Saiz, J.; Juan Císcar, A. (2014). Statistical text-to-speech synthesis of Spanish subtitles. En Advances in Speech and Language Technologies for Iberian Languages: Second International Conference, IberSPEECH 2014, Las Palmas de Gran Canaria, Spain, November 19-21, 2014. Proceedings. Springer International Publishing. 40-48. https://doi.org/10.1007/978-3-319-13623-3_5S4048Ahocoder, http://aholab.ehu.es/ahocoderCoursera, http://www.coursera.orgHMM-Based Speech Synthesis System (HTS), http://hts.sp.nitech.ac.jpKhan Academy, http://www.khanacademy.orgAxelrod, A., He, X., Gao, J.: Domain adaptation via pseudo in-domain data selection. In: Proc. of EMNLP, pp. 355–362 (2011)Bottou, L.: Stochastic gradient learning in neural networks. In: Proceedings of Neuro-Nîmes 1991. EC2, Nimes, France (1991)Dahl, G.E., Yu, D., Deng, L., Acero, A.: Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Transactions on Audio, Speech, and Language Processing 20(1), 30–42 (2012)Erro, D., Sainz, I., Navas, E., Hernaez, I.: Harmonics plus noise model based vocoder for statistical parametric speech synthesis. IEEE Journal of Selected Topics in Signal Processing 8(2), 184–194 (2014)Fan, Y., Qian, Y., Xie, F., Soong, F.: TTS synthesis with bidirectional LSTM based recurrent neural networks. In: Proc. of Interspeech (submitted 2014)Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A.R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N., et al.: Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine 29(6), 82–97 (2012)Hunt, A.J., Black, A.W.: Unit selection in a concatenative speech synthesis system using a large speech database. In: Proc. of ICASSP, vol. 1, pp. 373–376 (1996)King, S.: Measuring a decade of progress in text-to-speech. Loquens 1(1), e006 (2014)Koehn, P.: Statistical Machine Translation. Cambridge University Press (2010)Kominek, J., Schultz, T., Black, A.W.: Synthesizer voice quality of new languages calibrated with mean mel cepstral distortion. In: Proc. of SLTU, pp. 63–68 (2008)Lopez, A.: Statistical machine translation. ACM Computing Surveys 40(3), 8:1–8:49 (2008)poliMedia: The polimedia video-lecture repository (2007), http://media.upv.esSainz, I., Erro, D., Navas, E., Hernáez, I., Sánchez, J., Saratxaga, I.: Aholab speech synthesizer for albayzin 2012 speech synthesis evaluation. In: Proc. of IberSPEECH, pp. 645–652 (2012)Seide, F., Li, G., Chen, X., Yu, D.: Feature engineering in context-dependent dnn for conversational speech transcription. In: Proc. of ASRU, pp. 24–29 (2011)Shinoda, K., Watanabe, T.: MDL-based context-dependent subword modeling for speech recognition. Journal of the Acoustical Society of Japan 21(2), 79–86 (2000)Silvestre-Cerdà, J.A., et al.: Translectures. In: Proc. of IberSPEECH, pp. 345–351 (2012)TED Ideas worth spreading, http://www.ted.comThe transLectures-UPV Team.: The transLectures-UPV toolkit (TLK), http://translectures.eu/tlkToda, T., Black, A.W., Tokuda, K.: Mapping from articulatory movements to vocal tract spectrum with Gaussian mixture model for articulatory speech synthesis. In: Proc. of ISCA Speech Synthesis Workshop (2004)Tokuda, K., Kobayashi, T., Imai, S.: Speech parameter generation from hmm using dynamic features. In: Proc. of ICASSP, vol. 1, pp. 660–663 (1995)Tokuda, K., Masuko, T., Miyazaki, N., Kobayashi, T.: Multi-space probability distribution HMM. IEICE Transactions on Information and Systems 85(3), 455–464 (2002)transLectures: D3.1.2: Second report on massive adaptation, http://www.translectures.eu/wp-content/uploads/2014/01/transLectures-D3.1.2-15Nov2013.pdfTurró, C., Ferrando, M., Busquets, J., Cañero, A.: Polimedia: a system for successful video e-learning. In: Proc. of EUNIS (2009)Videolectures.NET: Exchange ideas and share knowledge, http://www.videolectures.netWu, Y.J., King, S., Tokuda, K.: Cross-lingual speaker adaptation for HMM-based speech synthesis. In: Proc. of ISCSLP, pp. 1–4 (2008)Yamagishi, J.: An introduction to HMM-based speech synthesis. Tech. rep. Centre for Speech Technology Research (2006), https://wiki.inf.ed.ac.uk/twiki/pub/CSTR/TrajectoryModelling/HTS-Introduction.pdfYoshimura, T., Tokuda, K., Masuko, T., Kobayashi, T., Kitamura, T.: Simultaneous modeling of spectrum, pitch and duration in HMM-based speech synthesis. In: Proc. of Eurospeech, pp. 2347–2350 (1999)Zen, H., Senior, A.: Deep mixture density networks for acoustic modeling in statistical parametric speech synthesis. In: Proc. of ICASSP, pp. 3872–3876 (2014)Zen, H., Senior, A., Schuster, M.: Statistical parametric speech synthesis using deep neural networks. 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A Shallow Parsing Approach to Natural Language Queries of a Database

Copyright © 2019 by author(s) and Scientific Research Publishing Inc. The performance and reliability of converting natural language into structured query language can be problematic in handling nuances that are prevalent in natural language. Relational databases are not designed to understand language nuance, therefore the question why we must handle nuance has to be asked. This paper is looking at an alternative solution for the conversion of a Natural Language Query into a Structured Query Language (SQL) capable of being used to search a relational database. The process uses the natural language concept, Part of Speech to identify words that can be used to identify database tables and table columns. The use of Open NLP based grammar files, as well as additional configuration files, assist in the translation from natural language to query language. Having identified which tables and which columns contain the pertinent data the next step is to create the SQL statement

Using same-language machine translation to create alternative target sequences for text-to-speech synthesis

Modern speech synthesis systems attempt to produce speech utterances from an open domain of words. In some situations, the synthesiser will not have the appropriate units to pronounce some words or phrases accurately but it still must attempt to pronounce them. This paper presents a hybrid machine translation and unit selection speech synthesis system. The machine translation system was trained with English as the source and target language. Rather than the synthesiser only saying the input text as would happen in conventional synthesis systems, the synthesiser may say an alternative utterance with the same meaning. This method allows the synthesiser to overcome the problem of insufficient units in runtime