Kws Literature Review Continued

Some more literature review of keyword detection:

• Chen, Guoguo, Carolina Parada, and Tara N. Sainath. “Query-by-example keyword spotting using long short-term memory networks.” Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on. IEEE, 2015.
  • They use a long short-term memory (LSTM) neural netowrk for KWS.
  • This allows for training new keywords, beyond just those where we have prior knowledge.
  • Feature Extraction:
    • VAD used to reduce computation st. KWS only run on voiced regions.
      • 13-dimensional PLP features
  • The found that this approach reduces the false rejection rate by 86%.
• Chen, et al. “Low-Resource Keyword Search Strategies for Tamil.” 2015 IEEE International Conference on. IEEE, 2015.
  • They propose three strategies for low-research KWS.
    • Submodular Optimization to Select Audio to Transcribe
      • for-each utterance, s, in a set S they measure the degree to with s contains some feature u. This can be used to determine the probability distribution of that feature.
    • Keyword Aware Language Modeling
    • Word Morph Interpolated Language Model
      • 3 language models are constructed
        • Word based LM, which is trained on all word entries
        • Morph (automatically parsed morphemes) based LM, which is trained by parsing word entries into morphs
        • Hybrid Word-Morph LM, where words with more than one occurrence are retained and words with one occurrence are parsed into morphs.