First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Summary: The paper is motivated by a recent Plos CB paper by Makin et al 2013, which used numerical simulations of a particular latent variable model that (in this model), performing density estimation of sensory population activity yielded latent variables which retained all the information about the underlying stimulus-variable. In the present paper, the authors ask the question of whether these results hold more gernally. They prove mathematically (assuming some sufficient conditions) that this results holds for a range of models, but also provide counter-examples that it does not always hold. Quality: Results seem sound, but I did not check the proof very rigorously.

We have developed visual preprocessing algorithms for extracting phonologically relevant features from the grayscale video image of a speaker, to provide speaker-independent inputs for an automat(cid:173) ic lipreading ("speechreading") system. Visual features such as mouth open/closed, tongue visible/not-visible, teeth visible/not(cid:173) visible, and several shape descriptors of the mouth and its motion are all rapidly computable in a manner quite insensitive to lighting conditions. We formed a hybrid speechreading system consisting of two time delay neural networks (video and acoustic) and inte(cid:173) grated their responses by means of independent opinion pooling - the Bayesian optimal method given conditional independence, which seems to hold for our data. This hybrid system had an er(cid:173) ror rate 25% lower than that of the acoustic subsystem alone on a five-utterance speaker-independent task, indicating that video can be used to improve speech recognition.

Automated speech recognition is notoriously hard, and thus any predictive source of information and constraints that could be incorporated into a computer speech recognition system would be desirable. Humans, especially the hearing impaired, can utilize visual information - "speech reading" - for improved accuracy (Dodd & Campbell, 1987, Sanders & Goodrich, 1971). Speech reading can provide direct information about segments, phonemes, rate, speaker gender and identity, and subtle informationfor segmenting speech from background noise or multiple speakers (De Filippo & Sims, 1988, Green & Miller, 1985). Fundamental support for the use of visual information comes from the complementary natureof the visual and acoustic speech signals. Utterances that are difficult to distinguish acoustically are the easiest to distinguish.

Automated speech recognition is notoriously hard, and thus any predictive source of information and constraints that could be incorporated into a computer speech recognition system would be desirable. Humans, especially the hearing impaired, can utilize visual information - "speech reading" - for improved accuracy (Dodd & Campbell, 1987, Sanders & Goodrich, 1971). Speech reading can provide direct information about segments, phonemes, rate, speaker gender and identity, and subtle information for segmenting speech from background noise or multiple speakers (De Filippo & Sims, 1988, Green & Miller, 1985). Fundamental support for the use of visual information comes from the complementary nature of the visual and acoustic speech signals. Utterances that are difficult to distinguish acoustically are the easiest to distinguish.

Automated speech recognition is notoriously hard, and thus any predictive source of information and constraints that could be incorporated into a computer speech recognition system would be desirable. Humans, especially the hearing impaired, can utilize visual information - "speech reading" - for improved accuracy (Dodd & Campbell, 1987, Sanders & Goodrich, 1971). Speech reading can provide direct information about segments, phonemes, rate, speaker gender and identity, and subtle information for segmenting speech from background noise or multiple speakers (De Filippo & Sims, 1988, Green & Miller, 1985). Fundamental support for the use of visual information comes from the complementary nature of the visual and acoustic speech signals. Utterances that are difficult to distinguish acoustically are the easiest to distinguish.