Saudi Accented Arabic Voice Bank

AbstractThe aim of this paper is to present an Arabic speech database that represents Arabic native speakers from all the cities of Saudi Arabia. The database is called the Saudi Accented Arabic Voice Bank (SAAVB). Preparing the prompt sheets, selecting the right speakers and transcribing their speech are some of the challenges that faced the project team. The procedures that meet these challenges are highlighted. SAAVB consists of 1033 speakers speak in Modern Standard Arabic with a Saudi accent. The SAAVB content is analyzed and the results are illustrated. The content was verified internally and externally by IBM Cairo and can be used to train speech engines such as automatic speech recognition and speaker verification systems

Design of a phonetic corpus for speech recognition in catalan

In this paper, we present the design of a corpus for speech recognition to be used for the recording of a speech database in Catalan. A previous database in Spanish was the reference in setting the specifications about the characteristics of the sentences and in the minimum number of units required. An analysis of unit frequencies were carried out in order to know which units were relevant for training and to compare the results with the figures from the designed corpus. Three different sub-corpora were generated, one for training, ...Peer ReviewedPostprint (published version

The EASR corpora of European Portuguese, French, Hungarian and Polish elderly speech

Currently available speech recognisers do not usually work well with elderly speech. This is because several characteristics of speech (e.g. fundamental frequency, jitter, shimmer and harmonic noise ratio) change with age and because the acoustic models used by speech recognisers are typically trained with speech collected from younger adults only. To develop speech-driven applications capable of successfully recognising elderly speech, this type of speech data is needed for training acoustic models from scratch or for adapting acoustic models trained with younger adults’ speech. However, the availability of suitable elderly speech corpora is still very limited. This paper describes an ongoing project to design, collect, transcribe and annotate large elderly speech corpora for four European languages: Portuguese, French, Hungarian and Polish. The Portuguese, French and Polish corpora contain read speech only, whereas the Hungarian corpus also contains spontaneous command and control type of speech. Depending on the language in question, the corpora contain 76 to 205 hours of speech collected from 328 to 986 speakers aged 60 and over. The final corpora will come with manually verified orthographic transcriptions, as well as annotations for filled pauses, noises and damaged words.info:eu-repo/semantics/acceptedVersio

On the voice-activated question answering

[EN] Question answering (QA) is probably one of the most challenging tasks in the field of natural language processing. It requires search engines that are capable of extracting concise, precise fragments of text that contain an answer to a question posed by the user. The incorporation of voice interfaces to the QA systems adds a more natural and very appealing perspective for these systems. This paper provides a comprehensive description of current state-of-the-art voice-activated QA systems. Finally, the scenarios that will emerge from the introduction of speech recognition in QA will be discussed. © 2006 IEEE.This work was supported in part by Research Projects TIN2009-13391-C04-03 and TIN2008-06856-C05-02. This paper was recommended by Associate Editor V. Marik.Rosso, P.; Hurtado Oliver, LF.; Segarra Soriano, E.; Sanchís Arnal, E. (2012). On the voice-activated question answering. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews. 42(1):75-85. https://doi.org/10.1109/TSMCC.2010.2089620S758542

Learning morphology with Morfette

Morfette is a modular, data-driven, probabilistic system which learns to perform joint morphological tagging and lemmatization from morphologically annotated corpora. The system is composed of two learning modules which are trained to predict morphological tags and lemmas using the Maximum Entropy classifier. The third module dynamically combines the predictions of the Maximum-Entropy models and outputs a probability distribution over tag-lemma pair sequences. The lemmatization module exploits the idea of recasting lemmatization as a classification task by using class labels which encode mappings from wordforms to lemmas. Experimental evaluation results and error analysis on three morphologically rich languages show that the system achieves high accuracy with no language-specific feature engineering or additional resources

The EASR Corpora of European Portuguese, French, Hungarian and Polish elderly speech

Currently available speech recognisers do not usually work well with elderly speech. This is because several characteristics of speech (e.g. fundamental frequency, jitter, shimmer and harmonic noise ratio) change with age and because the acoustic models used by speech recognisers are typically trained with speech collected from younger adults only. To develop speech-driven applications capable of successfully recognising elderly speech, this type of speech data is needed for training acoustic models from scratch or for adapting acoustic models trained with younger adults’ speech. However, the availability of suitable elderly speech corpora is still very limited. This paper describes an ongoing project to design, collect, transcribe and annotate large elderly speech corpora for four European languages: Portuguese, French, Hungarian and Polish. The Portuguese, French and Polish corpora contain read speech only, whereas the Hungarian corpus also contains spontaneous command and control type of speech. Depending on the language in question, the corpora contain 76 to 205 hours of speech collected from 328 to 986 speakers aged 60 and over. The final corpora will come with manually verified orthographic transcriptions, as well as annotations for filled pauses, noises and damaged words.info:eu-repo/semantics/publishedVersio

Moroccan dialect NLP resources for Data Engineering and Intelligent Systems

Dialectal resources can provide valuable information for scientific research in many domains. They can play an important role in scientific research, especially in the fields of linguistics, sociology, anthropology, psychology, digital transformation, and artificial intelligence. NLP can also play an important role in decision-making by enabling the analysis of large volumes of textual data to extract relevant information. Data relevance is a key factor in decision-making, and companies wishing to join the trend must have all NLP resources at their disposal. In this paper, we will present the different resources and systems developed for Data Engineering and Intelligent Systems for Moroccan dialects