Efficient Algorithms for Speech Recognition by Ravishankar M.K.

By Ravishankar M.K.

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However, not all transitions have explicit trigram or even bigram probabilities in the language model. 2. 6: Word Transitions in Sphinx-II Baseline System. Therefore, the computation is approximated by using trigram and bigram transitions that can actually be found in the grammar, and backing o to unigrams through a backo node for the rest . Thus, the total number of transitions evaluated is at most V plus the number of bigrams and trigrams for the n words exited, which is a typically a much smaller number than nV .

The backward pass identi es several beginning times for a word, but typically only one ending time. Acoustic scores for each word segmentation are available in the backward pass word lattice. 2 A* Search The A* search algorithm is described in 42 . It works by maintaining an ordered stack or list of partial hypotheses, sorted in descending order of likelihood. Hypotheses are word sequences and may be of di erent lengths, accounting for di erent lengths of input speed. 7 outlines the basic stack decoding algorithm for nding N -best hypotheses.

Therefore, the parallel set of models at the end of any given word are not all unique. By removing duplicates, the fanout can be further reduced. In Sphinx-II, these two factors together reduce the right context fanout by about 70 on average. The increase is number of rightmost triphones is partly o set by the reduction in computation a orded by the sharper triphone models. 30 CHAPTER 3. THE SPHINX-II BASELINE SYSTEM Left Context The phonetic left context for the rst phone position in a word is the last base phone from the previous word.

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