The aims of the study described in this paper are (1) to assess the relative speaker discriminant properties of phonemes and ( 2 ) to investigate the importance of the temporal frame-to-frame information for speaker modelling in the framework of a text-prompted speaker verification system using Hidden Markov Models (HMMs) and Multi Layer Perceptrons (MLPs). It is shown that, with similar experimental conditions, nasals, fricatives and vowels convey more speaker specific informations than plosives and liquids. Regarding the influence of f he frame-to-frame temporal information, significant improvements are reported from the inclusion of several acoustic frames at the input ofthe MLPs. Results tend also to show that each phoneme has its optimal M I 2 context size giving the best Equal Error Rate (EER).

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In this paper we propose a generic framework to index and retrieve audio. In this framework, audio data is transformed into a sequence of symbols using the ALISP tools. In such a way the audio data is represented in a compact way. Then an approximate matching algorithm inspired from the BLAST technique is exploited to retrieve the majority of audio items that could be present in radio stream. The evaluations of the proposed systems are done on a private radio broadcast database provided by YACAST and other publicly available corpora. The experimental results show an excellent performance in audio identification (for advertisement and songs), audio motif discovery (for advertisement and songs), speaker diarization and laughter detection. Moreover, the ALISP-based system has obtained the best results in ETAPE 2011 (Evaluations en Traitement Automatique de la Parole) evaluation campaign for the speaker diarization task.

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