Machine and acoustical condition dependency analyses for fast acoustic likelihood calculation techniques

The acceleration of acoustic likelihood calculation has been an important research issue for developing practical speech recognition systems. And there are various specification machines and various acoustical conditions in the fields to which speech recognition is applied. In this paper, we reveal the machine and acoustical condition dependencies of fast acoustic likelihood calculation techniques. We employed state likelihood recycling as an approximation technique, batch state likelihood calculation as a technique based on computer architecture, and their combinations with or without acoustic backing-off that were our previously proposed efficient techniques. We evaluated and analyzed these four techniques in large vocabulary continuous speech recognition experiments by using four machines with different types of CPUs (Intel Pentium 4, Xeon, Core 2 Duo and Xeon X5570) under two acoustical conditions (clean and noisy). The combined technique with acoustic backing-off exhibited the best acceleration performance while preventing word accuracy degradation under all of the experimental conditions. The experimental and analytical results obtained in this paper are informative especially for developing speech recognition systems that are used in the fields.