Large Vocabulary Continuous Speech Recognition

1. Large VocabularyContinuous Speech Recognition

2. Subword Speech Units

5. HMM-Based Subword Speech Units

7. Training of Subword Units

8. Training of Subword Units

10. Training Procedure

14. Errors and performance evaluation in PLU recognition • Substitution error (s) • Deletion error (d) • Insertion error (i) • Performance evaluation: • If the total number of PLUs is N, we define: • Correctness rate: N – s – d /N • Accuracy rate: N – s – d – i / N

15. Language Models for LVCSR Word Pair Model: Specify which word pairs are valid

16. Statistical Language Modeling

17. Perplexity of the Language Model Entropy of the Source: First order entropy of the source: If the source is ergodic, meaning its statistical properties can be completely characterized in a sufficiently long sequence that the Source puts out,

18. We often compute H based on a finite but sufficiently large Q: H is the degree of difficulty that the recognizer encounters, on average, When it is to determine a word from the same source. Using language model, if the N-gram language model PN(W) is used, An estimate of H is: In general: Perplexity is defined as:

19. Overall recognition system based on subword units

20. Naval Resource (Battleship) Management Task: 991-word vocabulary NG (no grammar): perplexity = 991

21. Word pair grammar We can partition the vocabulary into four nonoverlapping sets of words: The overall FSN allows recognition of sentences of the form:

22. WP (word pair) grammar: Perplexity=60 FSN based on Partitioning Scheme: 995 real arcs and 18 null arcs WB (word bigram) Grammar: Perplexity =20

23. Control of word insertion/word deletion rate • In the discussed structure, there is no control on the sentence length • We introduce a word insertion penalty into the Viterbi decoding • For this, a fixed negative quantity is added to the likelihood score at the end of each word arc

28. Context-dependent subword units Creation of context-dependent diphones and triphones

29. If c(.) is the occurrence count for a given unit, we can use a unit reduction rule such as: CD units using only intraword units for “show all ships”: CD units using both intraword and itnerword units:

31. Smoothing and interpolation of CD PLU models

32. Implementation issues using CD units

35. Word junction effects To handle known phonological changes, a set of phonological rules are Superimposed on both the training and recognition networks. Some typical phonological rules include:

36. Recognition results using CD units

38. Position dependent units

39. Unit splitting and clustering

45. A key source of difficulty in continuous speech recognition is the So-called function words, which include words like a, and, for, in, is. The function words have the following properties:

46. Creation of vocabulary-independent units

47. Semantic Postprocessor For Recognition