Lori Lamel is a senior research scientist at the CNRS, which she joined as a permanent researcher at the Computer Science Laboratory for Mechanics and Engineering Sciences (LIMSI - http://www.limsi.fr/index.en.html). Her current activities fall in the areas of research in speaker-independent, large vocabulary continuous speech recognition; studies in acoustic-phonetics; lexical and phonological modeling; design, analysis, and realization of large speech corpora (TIMIT, BREF, TED); speaker and language identification.
This talk will highlight some open challenges in automatic processing of spoken language. Automatic speech processing has witnessed substantial advances over the last decade with growing interest in transcription systems for automatic structuring of audio supporting a variety of applications such as information archival and retrieval, media monitoring, automatic subtitling, question-answering, speech translation and speech analytics. Much of the information on the web is not in a textual format, and therefore will escape detection and categorization via text-based methods. Today's systems rely on large quantities of audio and textual data for model training, which often entail high development costs. Therefore research is needed on developing generic recognition models and automatic learning from unannotated data. Important future research will address keeping language models up-to-date, automatic topic identification, and enriched transcriptions providing annotations for speaker turns, language, acoustic conditions, etc. While the performance of speech recognition technology has improved dramatically for a number of 'dominant' languages, technologies for language and speech processing are available only for a small proportion of the world's languages. A major challenge is to have a wider coverage of languages, allowing all citizens to interact in their own native language. With more and more connectivity and mobility, a related challenge is dealing with language (code) switching.