Automatic determination of sub-word units for automatic speech recognition

Current automatic speech recognition (ASR) research is focused on recognition of continuous, spontaneous speech. Spontaneous speech contains a lot of variability in the way words are pronounced, and canonical pronunciations of each word are not true to the variation that is seen in real data. Two of the components of an ASR system are acoustic models and pronunciation models. The variation within spontaneous speech must be accounted for by these components. Phones, or context-dependent phones are typically used as the base subword unit, and one acoustic model is trained for each sub-word unit. Pronunciation modelling largely takes place in a dictionary, which relates words to sequences of phones. Acoustic modelling and pronunciation modelling overlap, and the two are not clearly separable in modelling pronunciation variation. Techniques that find pronunciation variants in the data and then reflect these in the dictionary have not provided expected gains in recognition. An alternative approach to modelling pronunciations in terms of phones is to derive units automatically: using data-driven methods to determine an inventory of sub-word units, their acoustic models, and their relationship to words. This thesis presents a method for the automatic derivation of a sub-word unit inventory, whose main components are 1. automatic and simultaneous generation of a sub-word unit inventory and acoustic model set, using an ergodic hidden Markov model whose complexity is controlled using the Bayesian Information Criterion 2. automatic generation of probabilistic dictionaries using joint multigrams The prerequisites of this approach are fewer than in previous work on unit derivation; notably, the timings of word boundaries are not required here. The approach is language independent since it is entirely data-driven and no linguistic information is required. The dictionary generation method outperforms a supervised method using phonetic data. The automatically derived units and dictionary perform reasonably on a small spontaneous speech task, although not yet outperforming phones

The direction of technical change in AI and the trajectory effects of government funding

Government funding of innovation can have a significant impact not only on the rate of technical change, but also on its direction. In this paper, we examine the role that government grants and government departments played in the development of artificial intelligence (AI), an emergent general purpose technology with the potential to revolutionize many aspects of the economy and society. We analyze all AI patents filed at the US Patent and Trademark Office and develop network measures that capture each patent’s influence on all possible sequences of follow-on innovation. By identifying the effect of patents on technological trajectories, we are able to account for the long-term cumulative impact of new knowledge that is not captured by standard patent citation measures. We show that patents funded by government grants, but above all patents filed by federal agencies and state departments, profoundly influenced the development of AI. These long-term effects were especially significant in early phases, and weakened over time as private incentives took over. These results are robust to alternative specifications and controlling for endogeneity