The recognition rate for fluently spoken speech achieved with beam search algorithms depend on factors as the perplexity and the recombination structure of the language model, the difficulty of the vocabulary, the quality of the acoustic phonetic decoder, and the chosen pruning strategy. In this paper a statistical model taking these factors in account is develloped. An iterative algorithm to calculate the score distribution of beam search paths and the resulting sentence error rate is presented.

This paper presents a statistical model for analyzing word-hypothesing performance in beam search algorithms, considering factors like language model complexity, vocabulary difficulty, acoustic phonetic decoder quality, and pruning strategy.

Statistical analysis of left-to-right parser for word-hypothesing

Key Takeaway: This paper presents a statistical model for analyzing word-hypothesing performance in beam search algorithms, considering factors like language model complexity, vocabulary difficulty, acoustic phonetic decoder quality, and pruning strategy.