Why has (reasonably accurate) Automatic Speech Recognition been so hard to achieve?

Hidden Markov models (HMMs) have been successfully applied to automatic speech recognition for more than 35 years in spite of the fact that a key HMM assumption -- the statistical independence of frames -- is obviously violated by speech data. In fact, this data/model mismatch has inspired many attempts to modify or replace HMMs with alternative models that are better able to take into account the statistical dependence of frames. However it is fair to say that in 2010 the HMM is the consensus model of choice for speech recognition and that HMMs are at the heart of both commercially available products and contemporary research systems. In this paper we present a preliminary exploration aimed at understanding how speech data depart from HMMs and what effect this departure has on the accuracy of HMM-based speech recognition. Our analysis uses standard diagnostic tools from the field of statistics -- hypothesis testing, simulation and resampling -- which are rarely used in the field of speech recognition. Our main result, obtained by novel manipulations of real and resampled data, demonstrates that real data have statistical dependency and that this dependency is responsible for significant numbers of recognition errors. We also demonstrate, using simulation and resampling, that if we `remove' the statistical dependency from data, then the resulting recognition error rates become negligible. Taken together, these results suggest that a better understanding of the structure of the statistical dependency in speech data is a crucial first step towards improving HMM-based speech recognition

Contemporary artists and colour: meaning, organisation and understanding

What implications do the ranges of traditional and non-traditional media used by contemporary artists have for understanding the selection and specification of coloured materials? Interviews with prominent artists explore their use of colour and their views on the role of colour in their work. The paper establishes that the interview respondents operate successfully within a professional and permeable frame of reference, with different approaches to determination of colour meaning. The colour propositions of neuroscience, psychophysics and anthropological linguistics appear to have little impact on the respondents’ practice, and the paper concludes by suggesting the need to explore boundaries between disciplines

Multilingual Language Processing From Bytes

We describe an LSTM-based model which we call Byte-to-Span (BTS) that reads text as bytes and outputs span annotations of the form [start, length, label] where start positions, lengths, and labels are separate entries in our vocabulary. Because we operate directly on unicode bytes rather than language-specific words or characters, we can analyze text in many languages with a single model. Due to the small vocabulary size, these multilingual models are very compact, but produce results similar to or better than the state-of- the-art in Part-of-Speech tagging and Named Entity Recognition that use only the provided training datasets (no external data sources). Our models are learning "from scratch" in that they do not rely on any elements of the standard pipeline in Natural Language Processing (including tokenization), and thus can run in standalone fashion on raw text

Managing the Complex Patient with Degenerative Cervical Myelopathy: How to Handle the Aging Spine, the Obese Patient, and Individuals with Medical Comorbidities.

Degenerative cervical myelopathy (DCM) is the most common cause of nontraumatic spinal cord injury worldwide. Even relatively mild impairment in functional scores can significantly impact daily activities. Surgery is an effective treatment for DCM, but outcomes are dependent on more than technique and preoperative neurologic deficits

Graz. Steiermark. Hauptplatz

Hauptplatz mit Erzherzog-Johann-Brunnendenkma