Section: New Results

Audio and speech content processing

Audio motif discovery

Participants : Frédéric Bimbot, Laurence Catanese, Armando Muscariello.

This work was performed in close collaboration with Guillaume Gravier from the Texmex project-team.

As an alternative to supervised approaches for multimedia content analysis, where predefined concepts are searched for in the data, we investigate content discovery approaches where knowledge emerge from the data. Following this general philosophy, we pursued work on motif discovery in audio contents.

Audio motif discovery is the task of finding out, without any prior knowledge, all pieces of signals that repeat, eventually allowing variability. In 2011, we extended our recent work on seeded discovery to near duplicate detection and spoken document retrieval from examples. First, we proposed alogirhtmic speed ups for the discovery of near duplicate motifs (low variability) in large (several days long) audio streams, exploiting subsampling strategies [muscariello-cbmi-11]. Second, we investigated the use of previously proposed efficient pattern matching techniques to deal with motif variability in speech data [muscariello-icassp-11] in a different setting, that of spoken document retrieval from an audio example. We demonstrated the potential of model-free approaches for efficient spoken document retrieval on a variety of data sets, in particular in the framework of the Spoken Web Search task of the MediaEval 2011 international evaluation [muscariello-is-11, muscariello-mediaeval-11].

This work is carried out in the context of the Quaero Project.

Landmark-driven speech recognition

Participant : Stefan Ziegler.

This work is supervised by Guillaume Gravier and Bogdan Ludusan from the Texmex project-team.

Speech recognition is a key issue to access multimedia spoken contents. In this context, speech recognition faces several challenges among which robustness to acoustic and linguistic variability.

In 2011, we initiated research on landmark-driven speech recognition to increase robustness. The idea of this approach consists in accurately detecting in the signal landmarks corresponding to broad phonetic classes (vowels, nasals, etc.). These landmarks, which represent almost certain knowledge about the phonetic content of the signal, are then used to bias the search space in Viterbi decoding towards solutions consistent with the landmarks. We proposed a landmark detection system, which employs numerous attributes extracted from a segment based representation of speech. We use a decision tree for BPC classification, since this allows the evaluation of each BPC on its most informative attributes, selected from a large variety of attributes. Then, each segment is converted into a landmark and a probability estimate for each BPC is provided. Second, we extend a previously proposed landmark-driven decoding strategy by a more flexible implementation, which reinforces paths at the detected landmarks according to the obtained BPC probabilities. Results obtained on French broadcast news data show a relative improvement in word error rate of about 2 % with respect to the baseline.