Life Sounds Extraction and Classification in Noisy Environment

International audienceThis paper deals with the sound event detection in a noisy environment and presents a first classification approach. Detection is the first step of our sound analysis system and is necessary to extract the significant sounds before ini-tiating the classification step. We present three original event detection algorithms. Among these algorithms, one is based on the wavelet and gives the best performances. We evaluate and compare their performance in a noisy en-vironment with the state of the art algorithms in the field. Then, we present a statistical study to obtain the acous-tical parameters necessary for the training and, the sound classification results. The detection algorithms and sound classification are applied to medical telemonitoring. We re-place video camera by microphones surveying life sounds in order to preserve patient's privacy

Sound Detection and Classification for Medical Telesurvey

International audienceMedical Telesurvey needs human operator assistance by smart information systems. This paper deals with the sound event detection in a noisy environment and presents a first classification approach. Detection is the first step of our sound analysis system and is necessary to extract the sig-nificant sounds before initiating the classification step. An algorithm based on the Wavelet Transform is evaluated in noisy environment. Then Wavelet based cepstral coeffi-cients are proposed and their results are compared with more classical parameters. Detection algorithm and sound classification methods are applied to medical telemonitor-ing. In our opinion, microphones surveying life sounds are better preserving patient privacy than video cameras

A Very Low Resource Language Speech Corpus for Computational Language Documentation Experiments

Most speech and language technologies are trained with massive amounts of speech and text information. However, most of the world languages do not have such resources or stable orthography. Systems constructed under these almost zero resource conditions are not only promising for speech technology but also for computational language documentation. The goal of computational language documentation is to help field linguists to (semi-)automatically analyze and annotate audio recordings of endangered and unwritten languages. Example tasks are automatic phoneme discovery or lexicon discovery from the speech signal. This paper presents a speech corpus collected during a realistic language documentation process. It is made up of 5k speech utterances in Mboshi (Bantu C25) aligned to French text translations. Speech transcriptions are also made available: they correspond to a non-standard graphemic form close to the language phonology. We present how the data was collected, cleaned and processed and we illustrate its use through a zero-resource task: spoken term discovery. The dataset is made available to the community for reproducible computational language documentation experiments and their evaluation.Comment: accepted to LREC 201

A small Griko-Italian speech translation corpus

This paper presents an extension to a very low-resource parallel corpus collected in an endangered language, Griko, making it useful for computational research. The corpus consists of 330 utterances (about 2 hours of speech) which have been transcribed and translated in Italian, with annotations for word-level speech-to-transcription and speech-to-translation alignments. The corpus also includes morpho syntactic tags and word-level glosses. Applying an automatic unit discovery method, pseudo-phones were also generated. We detail how the corpus was collected, cleaned and processed, and we illustrate its use on zero-resource tasks by presenting some baseline results for the task of speech-to-translation alignment and unsupervised word discovery. The dataset will be available online, aiming to encourage replicability and diversity in computational language documentation experiments

A cross-lingual adaptation approach for rapid development of speech recognizers for learning disabled users

Building a voice-operated system for learning disabled users is a difficult task that requires a considerable amount of time and effort. Due to the wide spectrum of disabilities and their different related phonopathies, most approaches available are targeted to a specific pathology. This may improve their accuracy for some users, but makes them unsuitable for others. In this paper, we present a cross-lingual approach to adapt a general-purpose modular speech recognizer for learning disabled people. The main advantage of this approach is that it allows rapid and cost-effective development by taking the already built speech recognition engine and its modules, and utilizing existing resources for standard speech in different languages for the recognition of the users’ atypical voices. Although the recognizers built with the proposed technique obtain lower accuracy rates than those trained for specific pathologies, they can be used by a wide population and developed more rapidly, which makes it possible to design various types of speech-based applications accessible to learning disabled users.This research was supported by the project ‘Favoreciendo la vida autónoma de discapacitados intelectuales con problemas de comunicación oral mediante interfaces personalizados de reconocimiento automático del habla’, financed by the Centre of Initiatives for Development Cooperation (Centro de Iniciativas de Cooperación al Desarrollo, CICODE), University of Granada, Spain. This research was supported by the Student Grant Scheme 2014 (SGS) at the Technical University of Liberec

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Résolution du problème inverse en métrologie optique par méthode statistique : Le Krigeage

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