Discriminative language model adaptation for Mandarin broadcast speech transcription and translation

This paper investigates unsupervised test-time adaptation of language models (LM) using discriminative methods for a Mandarin broadcast speech transcription and translation task. A standard approach to adapt interpolated language models to is to optimize the component weights by minimizing the perplexity on supervision data. This is a widely made approximation for language modeling in automatic speech recognition (ASR) systems. For speech translation tasks, it is unclear whether a strong correlation still exists between perplexity and various forms of error cost functions in recognition and translation stages. The proposed minimum Bayes risk (MBR) based approach provides a flexible framework for unsupervised LM adaptation. It generalizes to a variety of forms of recognition and translation error metrics. LM adaptation is performed at the audio document level using either the character error rate (CER), or translation edit rate (TER) as the cost function. An efficient parameter estimation scheme using the extended Baum-Welch (EBW) algorithm is proposed. Experimental results on a state-of-the-art speech recognition and translation system are presented. The MBR adapted language models gave the best recognition and translation performance and reduced the TER score by up to 0.54% absolute. © 2007 IEEE.

Semi-supervised video segmentation using tree structured graphical models

We present a novel, implementation friendly and occlusion aware semi-supervised video segmentation algorithm using tree structured graphical models, which delivers pixel labels alongwith their uncertainty estimates. Our motivation to employ supervision is to tackle a task-specific segmentation problem where the semantic objects are pre-defined by the user. The video model we propose for this problem is based on a tree structured approximation of a patch based undirected mixture model, which includes a novel time-series and a soft label Random Forest classifier participating in a feedback mechanism. We demonstrate the efficacy of our model in cutting out foreground objects and multi-class segmentation problems in lengthy and complex road scene sequences. Our results have wide applicability, including harvesting labelled video data for training discriminative models, shape/pose/articulation learning and large scale statistical analysis to develop priors for video segmentation. © 2011 IEEE.

The Cambridge Manufacturing Leaders' Programme

As with all Cambridge teaching, the Cambridge Manufacturing Leaders' Programme is based on one-to-one tutorial supervision, comprising guidance throughout a major strategic development project in the programme participant's company, interspersed with reflective study time spent in Cambridge. In this paper a description of the course is set in a wider philosophical context, looking at the role of work in a personal developmental sense, and the responsibility carried by manufacturing leaders for shaping and guiding that process. It is shown that the programme is rooted in and embodies important aspects of our European heritage regarding work as a learning process and the master/apprentice relationship as a way of giving educational guidance.

Real user evaluation of spoken dialogue systems using Amazon Mechanical Turk

This paper describes a framework for evaluation of spoken dialogue systems. Typically, evaluation of dialogue systems is performed in a controlled test environment with carefully selected and instructed users. However, this approach is very demanding. An alternative is to recruit a large group of users who evaluate the dialogue systems in a remote setting under virtually no supervision. Crowdsourcing technology, for example Amazon Mechanical Turk (AMT), provides an efficient way of recruiting subjects. This paper describes an evaluation framework for spoken dialogue systems using AMT users and compares the obtained results with a recent trial in which the systems were tested by locally recruited users. The results suggest that the use of crowdsourcing technology is feasible and it can provide reliable results. Copyright © 2011 ISCA.

Semantic transform: Weakly supervised semantic inference for relating visual attributes

Relative (comparative) attributes are promising for thematic ranking of visual entities, which also aids in recognition tasks. However, attribute rank learning often requires a substantial amount of relational supervision, which is highly tedious, and apparently impractical for real-world applications. In this paper, we introduce the Semantic Transform, which under minimal supervision, adaptively finds a semantic feature space along with a class ordering that is related in the best possible way. Such a semantic space is found for every attribute category. To relate the classes under weak supervision, the class ordering needs to be refined according to a cost function in an iterative procedure. This problem is ideally NP-hard, and we thus propose a constrained search tree formulation for the same. Driven by the adaptive semantic feature space representation, our model achieves the best results to date for all of the tasks of relative, absolute and zero-shot classification on two popular datasets. © 2013 IEEE.