Hiroaki Kokubo

Development of robust speech recognition middleware on microprocessor

We have developed speech recognition middleware on a RISC microprocessor which has robust processing functions against environmental noise and speaker differences. The speech recognition middleware enables developers and users to use a speech recognition process for many possible speech applications, such as car navigation systems and handheld PCs. We report implementation issues of speech recognition process in middleware of microprocessors and propose robust noise handling functions using ANC (adaptive noise cancellation) and noise adaptive models. We also propose a new speaker adaptation algorithm in which the relationships among HMMs (hidden Markov models) transfer vectors are provided as a set of pre-trained interpolation coefficients. Experimental evaluations on 1000-word vocabulary speech recognition showed promising results for both robust processing functions of the proposed noise handling methods and the proposed speaker adaptation method.

Compact and robust speech recognition for embedded use on microprocessors

We propose a compact and noise robust embedded speech recognition system implemented on microprocessors aiming for sophisticated HMIs (human machine interfaces) of car information systems. The compactness is essential for embedded systems because there are strict restrictions of CPU (central processing unit) power and available memory capacities. In this paper, first we report noise robust acoustic HMMs (hidden Markov models) and a compact spectral subtraction (SS) method after exhausting evaluation stages using real speech data recorded at car running environments. Next, we propose very novel memory assignment of acoustic models based on the product codes or sub-vector quantization technique resulting on 1 fourth memory reduction for the 2000-word vocabulary.

Sophisticated speech processing middleware on microprocessor

This paper describes speech processing middleware which has been developed on RISC microprocessors for embedded speech applications. This middleware consists of a speech recognition module and a speech synthesis module, and especially the speech recognition middleware has advantages of robustness for environmental noise and speaker differences. The speech middleware provides sophisticated user interfaces to multimedia systems using microprocessors as CPUs, such as car navigation systems, mobile information equipment, and game machines.

Real-Time Continuous Speech Recognition System on SH-4A Microprocessor

To expand CSR (continuous speech recognition) software to the mobile environmental use, we have developed embedded version of Julius (embedded Julius). Julius is open source CSR software, and has been used by many researchers and developers in Japan as a standard decoder on PCs. In this paper, we describe an implementation of the embedded Julius on a SH-4A microprocessor. SH-4A is a high-end 32-bit MPU (720 MIPS) with on-chip FPU. However, further computational reduction is necessary for the embedded Julius to operate realtime. Applying some optimizations, the embedded Julius achieves real-time processing on the SH-4A. The experimental results show 0.89 times RT(real-time), resulting 4.0 times faster than baseline CSR. We also evaluated the embedded Julius on large vocabulary (20,000 words). It shows almost real-time processing (1.25 times RT).

Embedded Julius on T-Engine Platform

In this paper, we report implemental results of an embedded version of Julius. We used T-Enginetrade as a hardware platform which has a SuperH microprocessor. The Julius is free and open continuous speech recognition (CSR) software running on personal computers (PCs) which have huge CPU power and storage memory size. The technical problems to make Julius for embedded version are computing/process and memory reductions of Julius software. We realized 2.23 of RTF (real time factor) of embedded speech recognition processing on the condition of 5000-word vocabulary without any recognition accuracy degradation