Team METISS

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Section: Application Domains

Keywords : speaker recognition, user authentication, voice signature, speaker adaptation, spoken document, speech modeling, speech recognition, rich transcription, beam-search, broadcast news indexing, audio-based multimodal structuring.

Spoken content processing

A number of audio signals contain speech, which conveys important information concerning the document origin, content and semantics. The field of speaker characterisation and verification covers a variety of tasks that consist in using a speech signal to determine some information concerning the identity of the speaker who uttered it.

In parallel, METISS maintains some know-how and develops new research in the area of acoustic modeling of speech signals and automatic speech transcription, mainly in the framework of the semantic analysis of audio and multimedia documents.

Robustness issues in speaker recognition

Speaker recognition and verification has made significant progress with the systematical use of probabilistic models, in particular Hidden Markov Models (for text-dependent applications) and Gaussian Mixture Models (for text-independent applications). As presented in the fundamentals of this report, the current state-of-the-art approaches rely on bayesian decision theory.

However, robustness issues are still pending : when speaker characteristics are learned on small quantities of data, the trained model has very poor performance, because it lacks generalisation capabilities. This problem can partly be overcome by adaptation techniques (following the MAP viewpoint), using either a speaker-independent model as general knowledge, or some structural information, for instance a dependency model between local distributions.

METISS also adresses a number of topics related to speaker characterisation, in particular speaker selection (i.e. how to select a representative subset of speakers from a larger population), speaker representation (namely how to represent a new speaker in reference to a given speaker population), speaker adaptation for speech recognition, and more recently, speaker's emotion detection.

Speech recognition for multi-modal indexing purposes

In multimodal documents, the audio track is generally a major source of information and, when it contains speech, it conveys a high level of semantic content. In this context, speech recognition functionalities are essential for the extraction of information relevant to the taks of content indexing.

As of today, there is no perfect technology able to provide an error-free speech retranscription and operating for any type of speech input. A current challenge is to be able to exploit the imperfect output of an Automatic Speech Recognition (ASR) system, using for instance Natural Language Processing (NLP) techniques, in order to extract structural (topic segmentation) and semantic (topic detection) information from the audio track.

Along the same line, another scientific challenge is to combine the ASR output with other sources of information coming from various modalities, in order to extract robust multi-modal indexes from a multimedia content (video, audio, textual metadata, etc...).


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