Team METISS

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Overall Objectives
Scientific Foundations
Application Domains
Software
New Results
Contracts and Grants with Industry
Other Grants and Activities
Dissemination
Bibliography
Inria / Raweb 2003
Project: METISS

Project : metiss

Section: Application Domains


Detecting, tracking and searching information in audio streams

Automatic tools to locate events in audio documents, structure them and browse through them as in textual documents are key issues in order to fully exploit most of the available audio documents (radio and television programmes and broadcasts, conference recordings, etc). In this respect, defining and extracting meaningful characteristics from an audio stream aim at obtaining a more or less structured representation of the document, thus facilitating content-based access or search by similarity. Activities in METISS focus on sound class characterisation and tracking in audio documents for a wide variety of features and documents.

Speaker detection

Keywords : audio stream , detection , tracking , segmentation , speaker recognition .

Participants : Frédéric Bimbot, Mathieu Ben, Guillaume Gravier.

Speaker characteristics, such as the gender, the approximate age, the accent or the identity, are key indices for the indexing of spoken documents. So are information concerning the presence or not of a given speaker in a document, the speaker changes, the presence of speech from multiple speakers, etc.

More precisely, the above mentioned tasks can be divided into three main categories: detecting the presence of a speaker in a document (classification problem); tracking the portions of a document corresponding to a speaker (temporal segmentation problem); segmenting a document into speaker turns (change detection problem).

These three problems are clearly closely related to the field of speaker characterisation, sharing many theoretical and practical aspects with the latter. In particular, all these application areas rely on the use of statistical tests, whether it is using the model of a speaker known to the system (speaker presence detection, speaker tracking) or using a model estimated on the fly (speaker segmentation). However, the specificities of the speaker detection task require the implementation of adequate solutions to neutralize the variability factors inherent to this task.

Detecting and tracking sound classes

Keywords : audio stream , detection , tracking , segmentation , audio indexing .

Participants : Guillaume Gravier, Michaël Betser, Frédéric Bimbot, Rémi Gribonval.

Locating various sounds or broad classes of sounds, such as silence, music or specific events like ball hits or a jingle, in an audio document is a key issue as far as automatic annotation of sound tracks is concerned. Indeed, specific audio events are crucial landmarks in a broadcast. Thus, locating automatically such events enables to answer a query by focusing on the portion of interest in the document or to structure a document for further processing. Typical sound tracks come from radio or TV broadcasts, or even movies.

In the continuity of research carried out at IRISA for many years (especially by Benveniste, Basseville, André-Obrecht, Delyon, Seck, ...) the statistical test approach can be applied to abrupt changes detection and sound class tracking, the latter provided a statistical model for each class to be detected or tracked was previously estimated. For example, detecting speech segments in the signal can be carried out by comparing the segment likelihoods using a speech and a ``non-speech'' statistical model respectively. The statistical models commonly used typically represent the distribution of the power spectral density, possibly including some temporal constraints if the audio events to look for show a specific time structure, as is the case with jingles or words. As an alternative to statistical tests, hidden Markov models can be used to simultaneously segment and classify an audio stream. In this case, each state (or group of states) of the automaton represent one of the audio event to be detected. As for the statistical test approach, the hidden Markov model approach requires that models, typically Gaussian mixture models, are estimated for each type of event to be tracked.

In the area of automatic detection and tracking of audio events, there are three main bottlenecks. The first one is the detection of simultaneous events, typically speech with music in a speech/music/noise segmentation problem since it is nearly impossible to estimate a model for each event combination. The second one is the not so uncommon problem of detecting very short events for which only a small amount of training data is available. In this case, the traditional 100 Hz frame analysis of the waveform and Gaussian mixture modeling suffer serious limitations. Finally, typical approaches require a preliminary step of manual annotation of a training corpus in order to estimate some model parameters. There is therefore a need for efficient machine learning and statistical parameter estimation techniques to avoid this tedious and costly annotation step.

Indexing using heterogeneous information

Keywords : audio stream , multimedia indexing , audiovisual integration , multimodality , information fusion .

Participants : Guillaume Gravier, Michaël Betser, Ewa Kijak.

Applied to the sound track of a video, detecting and tracking audio events, as mentioned in the previous section, can provide useful information about the video structure. Such information is by definition only partial and can seldom be exploited by itself for multimedia document structuring or abstracting. To achieve these goals, partial information from the various media must be combined. By nature, pieces of information extracted from different media are heterogeneous (text, topic, symbolic audio events, shot change, dominant color, etc.) thus making their integration difficult. Only recently approaches to combine audio and visual information in video structuring have appeared, most of them using very basic audio information.

Combining multimedia information can be performed at various level of abstraction. Currently, most approaches in video structuring rely on the combination of structuring events detected independently in each media. A popular way to combine information is the hierarchical approach which consists in using the results of the event detection of one media to provide cues for event detection in the other media. The Bayes detection theory also provides a powerful theoretical framework for a more integrated processing of heterogeneous information, in particular because this framework is already extensively exploited to detect structuring events in each media. Hidden Markov models with multiple observation streams also offer a great potential which has been experimented in audiovisual speech recognition so far [39] [42] [14].

The main research topics in this field are the definition of structuring events that should be detected on the one hand and the definition of statistical models to combine or to jointly model low-level heterogeneous information on the other hand. In particular, defining statistical models on low-level features is a promising idea as it avoids defining and detecting structuring elements independently for each media and enables an early integration of all the possible sources of information in the structuring process.

Speech modeling and recognition

Keywords : speech modeling , speech recognition , broadcast news indexing , beam-search .

Participant : Guillaume Gravier.

Many audio documents contain speech from which useful information concerning the document content can be extracted. However, extracting information from speech requires specific processing such as speech recognition or word spotting. Though speech recognition is not the main activity of METISS, some research efforts are made in the areas of acoustic modeling of speech signals and automatic speech transcription, mainly in order to complement our know-how in terms of audio segmentation and indexing within a realistic setup.

In particular, speech recognition is complementary with audio segmentation, speaker recognition and transaction security. In the first case, detecting speech segments in a continuous audio stream and segmenting the speech portions into pseudo-sentences is a preliminary step to automatic transcription. Detecting speaker changes and grouping together segments from the same speaker is also a crucial step for segmentation as for speaker adaptation. Speaker segmentation and tracking is often used to produce a rich transcription of an audio document, typically broadcast news, where the transcription contains speaker and topic indices in addition to the transcription. Last, in speaker recognition for secured transactions over the telephone, recognizing the linguistic content of the message might be useful, for example to hypothesize an identity, to recognize a spoken password or to extract linguistic parameters that can benefit to the speaker models.


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