SUMMARYWidespread use of large-vocabulary continuous speech recognition systems has recently occurred, encouraging the application of speech recognition techniques to various problems. One of the factors that adversely affect the performance of speech recognition systems is a mismatch between the acoustic properties of the speech of the system user and the acoustic model. The speech of young or middle-aged adults is generally used in constructing the acoustic model. Thus, a mismatch occurs between the model and the acoustic properties of the speech of the elderly, which may degrade the recognition rate. In this study, a large-scale elderly speech database (200 sentences × 301 subjects) is used to train the acoustic model, and the resulting elderly acoustic model is evaluated by using a large-vocabulary continuous speech recognition system. In the experiments, the word recognition rate was improved by 3 to 5% compared to the recognition results of an acoustic model trained by young or middle-aged adult speech, namely, by the JNAS speech database (150 sentences × 260 subjects, average 28.6 years). It is also verified experimentally that the recognition rate is further improved in speaker adaptation to elderly speech by making use of an acoustic model trained by elderly speech.

The Japanese Dictation Toolkit has been designed and developed as a baseline platform for Japanese LVCSR (Large Vocabulary Continuous Speech Recognition). The platform consists of a standard recognition engine, Japanese phone models and Japanese statistical language models. We set up a variety of Japanese phone HMMs from a contextindependent monophone to a triphone model of thousands of states. They are trained with ASJ (The Acoustical Society of Japan) databases. A lexicon and word N-gram (2-gram and 3-gram) models are constructed with a corpus of Mainichi newspaper. The recognition engine JULIUS is developed for evaluation of both acoustic and language models. As an integrated system of these modules, we have implemented a baseline 5,000-word dictation system and evaluated various components. The software repository is available to the public. +1

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Julius is a high-performance, two-pass LVCSR decoder for researchers and developers. Based on word 3-gram and context-dependent HMM, it can perform almost realtime decoding on most current PCs in 20k word dictation task. Major search techniques are fully incorporated such as tree lexicon, N-gram factoring, cross-word context dependency handling, enveloped beam search, Gaussian pruning, Gaussian selection, etc. Besides search efficiency, it is also modularized carefully to be independent from model structures, and various HMM types are supported such as shared-state triphones and tiedmixture models, with any number of mixtures, states, or phones. Standard formats are adopted to cope with other free modeling toolkit. The main platform is Linux and other Unix workstations, and partially works on Windows. Julius is distributed with open license together with source codes, and has been used by many researchers and developers in Japan.

To expand CSR (continuous speech recognition) software to the mobile environmental use, we have developed embedded version of "Julius". Julius is open source CSR software, and has been used by many researchers and developers in Japan as a standard decoder on PCs. Julius works as a real time decoder on a PC. However further computational reduction is necessary to use Julius on a microprocessor. Further cost reduction is needed. For reducing cost of calculating pdfs (probability density function), Julius adopts a GMS (Gaussian Mixture Selection) method. In this paper, we modify the GMS method to realize a continuous speech recognizer on microprocessors. This approach does not change the structure of acoustic models in consistency with that used by conventional Julius, and enables developers to use acoustic models developed by popular modeling tools. On simulation, the proposed method has archived 20% reduction of computational costs compared to conventional GMS, 40% reduction compared to no GMS. Finally, the embedded version of Julius was tested on a developmental hardware platform named "T-engine". The proposed method showed 2.23 of RTF (Real Time Factor) resulting 79% of that of no GMS without any degradation of recognition performance.