{"id":26215,"date":"2024-01-18T11:02:11","date_gmt":"2024-01-18T10:02:11","guid":{"rendered":"https:\/\/lium.univ-lemans.fr\/?p=26215"},"modified":"2024-01-22T11:16:18","modified_gmt":"2024-01-22T10:16:18","slug":"when-neural-speech-technologies-encounter-non-conventional-data-a-discussion-on-speech-recognition-and-speech-synthesis","status":"publish","type":"post","link":"https:\/\/lium.univ-lemans.fr\/en\/when-neural-speech-technologies-encounter-non-conventional-data-a-discussion-on-speech-recognition-and-speech-synthesis\/","title":{"rendered":"When neural speech technologies encounter non-conventional data: A discussion on speech recognition and speech synthesis"},"content":{"rendered":"

Seminar from Aghilas Sini (LIUM) <\/h2>\n

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Date:<\/strong> 19\/01\/2024
\nTime:<\/strong> 10h15
\nLocalization:<\/strong> IC2, Boardroom
\nSpeaker:<\/strong> Aghilas Sini<\/a>
\n 
\n <\/p>\n

When neural speech technologies encounter non-conventional data: A discussion on speech recognition and speech synthesis.<\/strong><\/p>\n

 <\/p>\n

Most neural speech technologies are developed using dedicated data recorded under favorable acoustic conditions. This data is instrumental in setting up the underlying models and facilitates a fair and straightforward comparison, enabling the establishment of benchmarks. However, it is intriguing to analyze and quantify the ability of neural speech systems to leverage non-dedicated data and real-world conditions, whether during the learning or inference stages.<\/p>\n

To address these questions and related issues, I will discuss the impact of non-conventional data on state-of-the-art speech technology through two specific practical examples: pronunciation assessment of children’s speech in a noisy classroom and the development of a fair speech synthesis system for the French language using amateur recording data. I will then explore two speech synthesis techniques, namely voice conversion and voice cloning, to investigate speaker identity and assess data quality.<\/p>\n

Furthermore, I will share ongoing and future work related to multimodal and multilingual data, particularly in the context of deep-fake speech detection and speech-to-speech translation. In conclusion, I will present reflections on the data qualification process, aiming to estimate and anticipate the performance of a given system.<\/p><\/div><\/div><\/div><\/div>

 <\/div>
 <\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"

Seminar from Aghilas Sini (LIUM)   Date: 19\/01\/2024 Time: 10h15 Localization: IC2, Boardroom Speaker: Aghilas Sini     When neural speech technologies encounter non-conventional data: A discussion on speech recognition and speech synthesis.   Most neural speech technologies are developed using dedicated data recorded under favorable acoustic conditions. This data is instrumental in setting up […]<\/p>\n

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