Publications by Fergus McInnes

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 models in both tandem and hybrid configuration with the use of multi-level adaptive networks, LHUC adaptation and Maxout units. The German system includes lightly supervised training and a new method for dictionary generation. Our voice activity detection system now uses a semi-Markov model to incorporate a prior on utterance lengths. There are improvements of up to 30% relative WER on the tst2013 English test set.

The recurrent neural network language model (RNNLM) has been demonstrated to consistently reduce perplexities and automatic speech recognition (ASR) word error rates across a variety of domains. In this paper we propose a pre-training method for the RNNLM, by sharing the output weights of the feed forward neural network language model (NNLM) with the RNNLM. This is accomplished by first fine-tuning the weights of the NNLM, which are then used to initialise the output weights of an RNNLM with the same number of hidden units. We have carried out text-based experiments on the Penn Treebank Wall Street Journal data, and ASR experiments on the TED talks data used in the International Workshop on Spoken Language Translation (IWSLT) evaluation campaigns. Across the experiments, we observe small improvements in perplexity and ASR word error rate.

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 can have a large impact on the accuracy of the ASR system; in this paper we demonstrate that it can have an even larger impact on downstream natural language processing tasks – in this case, machine translation. We develop a novel semi-Markov model which allows the segmentation of audio streams into speech utterances which are optimised for the desired distribution of sentence lengths for the target domain. We compare this with existing state-of-the-art methods and show that it is able to achieve not only improved ASR performance, but also to yield significant benefits to a speech translation task.

This paper presents the MUST-VIS system for the MediaMixer/VideoLectures.NET Temporal Segmentation and Annotation Grand Challenge. The system allows users to visualize a lecture as a series of segments represented by keyword clouds, with relations to other similar lectures and segments. Segmentation is performed using a multi-factor algorithm which takes advantage of the audio (through automatic speech recognition and word-based segmentation) and video (through the detection of actions such as writing on the blackboard). The similarity across segments and lectures is computed using a content-based recommendation algorithm. Overall, the graph-based representation of segment similarity appears to be a promising and cost-effective approach to navigating lecture databases.

This paper presents a new system for automatic transcription of lectures. The system combines a number of novel features, including deep neural network acoustic models using multi-level adaptive networks to incorporate out-of-domain information, and factored recurrent neural network language models. We demonstrate that the system achieves large improvements on the TED lecture transcription task from the 2012 IWSLT evaluation - our results are currently the best reported on this task, showing an relative WER reduction of more than 16% compared to the closest competing system from the evaluation.

In the inEvent EU project, we aim at structuring, retrieving, and sharing large archives of networked, and dynamically changing, multimedia recordings, mainly consisting of meetings, videoconferences, and lectures. More specifically, we are developing an integrated system that performs audiovisual processing of multimedia recordings, and labels them in terms of interconnected "hyper-events" (a notion inspired from hyper-texts). Each hyper-event is composed of simpler facets, including audio-video recordings and metadata, which are then easier to search, retrieve and share. In the present paper, we mainly cover the audio processing aspects of the system, including speech recognition, speaker diarization and linking (across recordings), the use of these features for hyper-event indexing and recommendation, and the search portal. We present initial results for feature extraction from lecture recordings using the TED talks.

This paper describes the University of Edinburgh (UEDIN) English ASR system for the IWSLT 2013 Evaluation. Notable features of the system include deep neural network acoustic models in both tandem and hybrid configuration, cross-domain adaptation with multi-level adaptive networks, and the use of a recurrent neural network language model. Improvements to our system since the 2012 evaluation - which include the use of a significantly improved n-gram language model - result in a 19% relative WER reduction on the set.

This paper describes the University of Edinburgh (UEDIN) systems for the IWSLT 2012 Evaluation. We participated in the ASR (English), MT (English-French, German-English) and SLT (English-French) tracks.

To date, most computational models of infant word segmentation have worked from phonemic or phonetic input, or have used toy datasets. In this paper, we present an algorithm for word extraction that works directly from naturalistic acoustic input: infant-directed speech from the CHILDES corpus. The algorithm identifies recurring acoustic patterns that are candidates for identification as words or phrases, and then clusters together the most similar patterns. The recurring patterns are found in a single pass through the corpus using an incremental method, where only a small number of utterances are considered at once. Despite this limitation, we show that the algorithm is able to extract a number of recurring words, including some that infants learn earliest, such as "Mommy" and the child’s name. We also introduce a novel information-theoretic evaluation measure.