No abstract

The training of state-of-the-art automatic speech recognition (ASR) systems requires huge relevant training corpora. The cost of such databases is high and remains a major limitation for the development of speech-enabled applications in particular contexts (e.g. low-density languages, or specialized domains). On the other hand, a large amount of data can be found in news prompts, movie subtitles or scripts, etc. The use of such data as training corpus could provide a low-cost solution to the acoustic model estimation problem. Unfortunately, prior transcripts are seldom exact with respect to the content of the speech signal, and suffer from a lack of temporal information. This paper tackles the issue of prompt-based speech corpora improvement, by addressing the problems mentioned above. We propose a method allowing to locate accurate transcript segments in speech signals and automatically correct errors or lack of transcript surrounding these segments. This method relies on a new decoding strategy where the search algorithm is driven by the imperfect transcription of the input utterances. The experiments are conducted on the French language, by using the ESTER database and a set of records (and associated prompts) from RTBF (Radio Télévision Belge Francophone). The results demonstrate the effectiveness of the proposed approach, in terms of both error correction and text-to-speech alignment.

Most of the Web-based methods for lexicon augmenting consist in capturing global semantic features of the targeted domain in order to collect relevant documents from the Web. We suggest that the local context of the out-of-vocabulary (OOV) words contains relevant information. We propose to use the Web to build locally-augmented lexicons which are used in a final local decoding pass. Our experiments confirm the relevance of the Web for the OOV word retrieval. Different methods are proposed to retrieve the hypothesis words. Finally we present the integration of new words in the transcription process based on part-of-speech models. This technique allows to recover 7.6% of the significant OOV words and the accuracy of the system is improved.

In this paper, we present a new method for video genre identification based on the linguistic content analysis. This approach relies on the analysis of the most frequent words in the video transcriptions provided by an automatic speech recognition system. Experiments are conducted on a corpus composed of cartoons, movies, news, commercials, documentary, sport and music. On this 7-genre identification task, the proposed transcription-based method obtains up to 80% of correct identification. Finally, this rate is increased to 95% by combining the proposed linguistic-level features with lowlevel acoustic features.Index Terms-video genre classification, audio-based video processing, linguistic feature extraction