The paper overviews the 11th evaluation campaign organized by the IWSLT workshop. The
2014 evaluation offered multiple tracks on lecture transcription and translation based on the …

This paper describes the Multi-Genre Broadcast (MGB) Challenge at ASRU 2015, an
evaluation focused on speech recognition, speaker diarization, and" lightly supervised" …

This paper investigates the use of multitask learning to improve context-dependent deep
neural network (DNN) acoustic models. The use of hybrid DNN systems with clustered …

This paper describes the University of Edinburgh's spoken language translation (SLT) and
machine translation (MT) systems for the IWSLT 2014 evaluation campaign. In the SLT track …

Speech segmentation is the problem of finding the end points of a speech utterance for
passing to an automatic speech recognition (ASR) system. The quality of this segmentation …

This paper describes the University of Edinburgh (UEDIN) ASR systems for the 2014 IWSLT
Evaluation. Notable features of the English system include deep neural network acoustic …

The recurrent neural network language model (RNNLM) has been demonstrated to
consistently reduce perplexities and automatic speech recognition (ASR) word error rates …

Recurrent neural network language models have been shown to consistently reduce the
word error rates (WERs) of large vocabulary speech recognition tasks. In this work we …

The Multi-Genre Broadcast Challenge at ASRU 2015 is a controlled evaluation of speech
recognition, speaker diarization, and lightly supervised alignment using BBC TV recordings …

This dissertation outlines work related to Speech Segmentation–segmenting an audio
recording into regions of speech and non-speech, and Speaker Diarization–further …