The cascade approach to Speech Translation (ST) is based on a pipeline that concatenates an Automatic Speech Recognition (ASR) system followed by a Machine Translation (MT) system. These systems are usually connected by a segmenter that splits the ASR output into, hopefully, semantically self-contained chunks to be fed into the MT system. This is specially challenging in the case of streaming ST, where latency requirements must also be taken into account. This work proposes novel segmentation models for streaming ST that incorporate not only textual, but also acoustic information to decide when the ASR output is split into a chunk. An extensive and thorough experimental setup is carried out on the Europarl-ST dataset to prove the contribution of acoustic information to the performance of the segmentation model in terms of BLEU score in a streaming ST scenario. Finally, comparative results with previous work also show the superiority of the segmentation models proposed in this work.
We introduce Europarl-ASR, a large speech and text corpus of parliamentary debates including 1 300 hours of transcribed speeches and 70 million tokens of text in English extracted from European Parliament sessions. The training set is labelled with the Parliament's non-fully-verbatim official transcripts, timealigned. As verbatimness is critical for acoustic model training, we also provide automatically noise-filtered and automatically verbatimized transcripts of all speeches based on speech data filtering and verbatimization techniques. Additionally, 18 hours of transcribed speeches were manually verbatimized to build reliable speaker-dependent and speaker-independent development/test sets for streaming ASR benchmarking. The availability of manual non-verbatim and verbatim transcripts for dev/test speeches makes this corpus useful for the assessment of automatic filtering and verbatimization techniques. This paper describes the corpus and its creation, and provides off-line and streaming ASR baselines for both the speaker-dependent and speaker-independent tasks using the three training transcription sets. The corpus is publicly released under an open licence.
Cada vez son más las universidades que apuestan por la producción de contenidos digitales como apoyo al aprendizaje en lı́nea o combinado en la enseñanza superior. El grupo de investigación MLLP lleva años trabajando junto al ASIC de la UPV para enriquecer estos materiales, y particularmente su accesibilidad y oferta lingüı́stica, haciendo uso de tecnologı́as del lenguaje como el reconocimiento automático del habla, la traducción automática y la sı́ntesis de voz. En este trabajo presentamos los pasos que se están dando hacia la traducción integral de estos materiales, concretamente a través del doblaje (semi-)automático mediante sistemas de sı́ntesis de voz adaptables al locutor.]
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