Voice To Text Presentation

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The document outlines a proposed voice to text mobile application project with the following goals: 1) Develop a voice to text converter for mobile devices within 6 months to assist people with limited mobility or visual impairments. 2) The voice to text system aims to have an accuracy level of at least 80%. 3) The application will be sold to major mobile companies for 99p per installation.

Voice to Text System Group M2 Kusa Oluseyi Mehdi Shahinmehr Sabeen Shahid Sugitha Ramkumar

Long-term Goal ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Customer Wishes ,[object Object],[object Object],[object Object],[object Object],[object Object]

Fulfilling Customer Needs ,[object Object],Run the application with voice command Input the text using voice or text Select the contacts with voice and send the message

Cost Analysis ,[object Object],[object Object],[object Object],[object Object],Sponsor Sponsor 11,960 ₤ 10%

Strengths and Advantages ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Next Steps of Action ,[object Object],[object Object],[object Object],[object Object]

Présentation Devoxx France 2014 Les nouvelles perspectives offertes par la reconnaissance vocale vont tôt ou tard nous confronter à l'usage de librairies spécialisées dont le fonctionnement interne nous échappe totalement. Comment dès lors les évaluer, les utiliser correctement et en tirer le meilleur parti ? Cette présentation ludique et pratique a pour objectif de démystifier les arcanes de l'analyse de la voix humaine et ses contraintes en vous présentant les rouages internes du logiciel libre "Recognito", créé par le conférencier et permettant l'identification d'un locuteur à partir de son empreinte vocale.

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This power-point presentation contains 45 slides. It describes SR system (a brief intro), what are the applications, the biological architecture of human speech recognition vs machine architecture, recognition process, flow summery of recognition process and the approaches to the SRS. All this is described in the first few slides (the first part, let's say), after that, this presentation describes the evolution process of SRS through the decades (the middle part), and at the last this presentation describes the machine learning approach in SRS. How neural net enhance the efficiency of a SRS.

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