A System for Simultaneous Translation of Lectures and Speeches

This thesis realizes the first existing automatic system for simultaneous speech-to-speech translation. The focus of this system is the automatic translation of (technical oriented) lectures and speeches from English to Spanish, but the different aspects described in this thesis will also be helpful for developing simultaneous translation systems for other domains or languages

Multimodal Probabilistic Person Tracking and Identification in Smart Spaces

In this thesis, a new methodology is introduced for the multimodal tracking and identification of multiple persons by seeking and integrating reliable ID cues whenever they become observable. The method opportunistically integrates person-specific identification cues that can only sparsely be observed for each person over time and keeps track of the location of identified persons while ID cues are not available

Improving Searchability of Automatically Transcribed Lectures Through Dynamic Language Modelling

Recording university lectures through lecture capture systems is increasingly common. However, a single continuous audio recording is often unhelpful for users, who may wish to navigate quickly to a particular part of a lecture, or locate a specific lecture within a set of recordings. A transcript of the recording can enable faster navigation and searching. Automatic speech recognition (ASR) technologies may be used to create automated transcripts, to avoid the significant time and cost involved in manual transcription. Low accuracy of ASR-generated transcripts may however limit their usefulness. In particular, ASR systems optimized for general speech recognition may not recognize the many technical or discipline-specific words occurring in university lectures. To improve the usefulness of ASR transcripts for the purposes of information retrieval (search) and navigating within recordings, the lexicon and language model used by the ASR engine may be dynamically adapted for the topic of each lecture. A prototype is presented which uses the English Wikipedia as a semantically dense, large language corpus to generate a custom lexicon and language model for each lecture from a small set of keywords. Two strategies for extracting a topic-specific subset of Wikipedia articles are investigated: a naïve crawler which follows all article links from a set of seed articles produced by a Wikipedia search from the initial keywords, and a refinement which follows only links to articles sufficiently similar to the parent article. Pair-wise article similarity is computed from a pre-computed vector space model of Wikipedia article term scores generated using latent semantic indexing. The CMU Sphinx4 ASR engine is used to generate transcripts from thirteen recorded lectures from Open Yale Courses, using the English HUB4 language model as a reference and the two topic-specific language models generated for each lecture from Wikipedia

Improving searchability of automatically transcribed lectures through dynamic language modelling

Recording university lectures through lecture capture systems is increasingly common. However, a single continuous audio recording is often unhelpful for users, who may wish to navigate quickly to a particular part of a lecture, or locate a specific lecture within a set of recordings. A transcript of the recording can enable faster navigation and searching. Automatic speech recognition (ASR) technologies may be used to create automated transcripts, to avoid the significant time and cost involved in manual transcription. Low accuracy of ASR-generated transcripts may however limit their usefulness. In particular, ASR systems optimized for general speech recognition may not recognize the many technical or discipline-specific words occurring in university lectures. To improve the usefulness of ASR transcripts for the purposes of information retrieval (search) and navigating within recordings, the lexicon and language model used by the ASR engine may be dynamically adapted for the topic of each lecture. A prototype is presented which uses the English Wikipedia as a semantically dense, large language corpus to generate a custom lexicon and language model for each lecture from a small set of keywords. Two strategies for extracting a topic-specific subset of Wikipedia articles are investigated: a naïve crawler which follows all article links from a set of seed articles produced by a Wikipedia search from the initial keywords, and a refinement which follows only links to articles sufficiently similar to the parent article. Pair-wise article similarity is computed from a pre-computed vector space model of Wikipedia article term scores generated using latent semantic indexing. The CMU Sphinx4 ASR engine is used to generate transcripts from thirteen recorded lectures from Open Yale Courses, using the English HUB4 language model as a reference and the two topic-specific language models generated for each lecture from Wikipedia. Three standard metrics – Perplexity, Word Error Rate and Word Correct Rate – are used to evaluate the extent to which the adapted language models improve the searchability of the resulting transcripts, and in particular improve the recognition of specialist words. Ranked Word Correct Rate is proposed as a new metric better aligned with the goals of improving transcript searchability and specialist word recognition. Analysis of recognition performance shows that the language models derived using the similarity-based Wikipedia crawler outperform models created using the naïve crawler, and that transcripts using similarity-based language models have better perplexity and Ranked Word Correct Rate scores than those created using the HUB4 language model, but worse Word Error Rates. It is concluded that English Wikipedia may successfully be used as a language resource for unsupervised topic adaptation of language models to improve recognition performance for better searchability of lecture recording transcripts, although possibly at the expense of other attributes such as readability

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference

Proceedings of the VIIth GSCP International Conference

The 7th International Conference of the Gruppo di Studi sulla Comunicazione Parlata, dedicated to the memory of Claire Blanche-Benveniste, chose as its main theme Speech and Corpora. The wide international origin of the 235 authors from 21 countries and 95 institutions led to papers on many different languages. The 89 papers of this volume reflect the themes of the conference: spoken corpora compilation and annotation, with the technological connected fields; the relation between prosody and pragmatics; speech pathologies; and different papers on phonetics, speech and linguistic analysis, pragmatics and sociolinguistics. Many papers are also dedicated to speech and second language studies. The online publication with FUP allows direct access to sound and video linked to papers (when downloaded)