Improving the Automatic Speech Recognition through the improvement of Laguage Models

Language models are one of the pillars on which the performance of automatic speech recognition systems are based. Statistical language models that use word sequence probabilities (n-grams) are the most common, although deep neural networks are also now beginning to be applied here. This is possible due to the increases in computation power and improvements in algorithms. In this paper, the impact that language models have on the results of recognition is addressed in the following situations: 1) when they are adjusted to the work environment of the final application, and 2) when their complexity grows due to increases in the order of the n-gram models or by the applica-tion of deep neural networks. Specifically, an automatic speech recognition system with different language models is applied to audio recordings, these corresponding to three experimental frameworks: formal orality, talk on newscasts, and TED talks in Galician. Experimental results showed that improving the quality of language models yields improvements in recognition performance.