Automatic Speech Recognition on Mobile Devices and over Communication Networks

Part of the series Advances in Pattern Recognition pp 233-254

Algorithm Optimizations: Low Memory Footprint

  • Marcel VasilacheAffiliated withNokia

* Final gross prices may vary according to local VAT.

Get Access

For speech recognition algorithms targeting mobile devices the memory footprint is a critical parameter. Although the memory consumption can be both static (long-term) and dynamic (run-time) in this chapter we focus mainly on the long-term memory requirements and, more specifically, on the techniques for acoustic model compression. As all compression methods, acoustic model compression is exploiting redundancies within the data as well as the limits for the parameter representation accuracy. Considering data redundancies specific for hidden Markov models (HMMs), parameter tying and state or density clustering algorithms are presented with cases like semicontinuous HMMs (SCHMMs) and subspace distribution clustered HMMs (SDCHMMs). Regarding parameter representation a simple scalar quantized representation is shown for the case of quantized HMMs (qHMMs). The effects on computational complexity are also reviewed for all the compression methods presented.