Embedded Speech Recognition System Research Articles

Efficient robust speech recognition with empirical mode decomposition using an FPGA chip with dual core

The purpose of this paper is to accelate the computing speed of Empirical Mode Decomposition (EMD) based on multi-core embedded systems for robust speech recognition. A reconfigurable chip, Field Programmable Gate Array (FPGA), is used for the implementation of the designed system. This paper applies EMD to discompose some noised speech signals into several Intrinsic Mode Functions (IMFs). These IMFs will be combined to recover the original speech by multiplying their corresponding weights which were trained by Genetic Algorithms (GA). After applying Empirical Mode Decomposition (EMD), we obtain a cleaner speech for recognition. Due to the complexity of the computation of the EMD, a dual-core architecture of embedded system on FPGA is proposed to accelerate the computing speed of EMD for robust speech recognition. This will enhance the efficiency of embedded speech recognition system.

Embedded Speaker Recognition System Design and Implementation Based on FPGA

For the hardcore processor such as DSP, the existence of embedded speech recognition system taking more time on train and recognition, this paper presents an FPGA-based platform with the principle of vector quantization speech recognition system implementations. In vector quantization using genetic algorithm for speaker recognition systems, the parallel hardware structure of the program can greatly reduce the calculation the time-consuming. After testing, the implementation program under the premise of ensuring the recognition rate, which can effectively reduce the time-consuming of the training and recognition.

Embedded Speech Recognition Based on Multiclass Support Vector Machine

This paper introduces the characteristics and requirements of speech recognition technology based on embedded platform. It also describes the basic theory and related properties of Support Vector Machine. The advantages and disadvantages of the Multiclass SVM algorithms are analyzed, providing the algorithms principles for training and recognition of SVM application in the embedded speech recognition system. Finally, we proposed a design strategy based on multiclass SVM decision tree classifier, combined with the features of the embedded speech recognition.

Maximum Likelihood Training and Adaptation of Embedded Speech Recognizers for Mobile Environments

For the acoustic models of embedded speech recognition systems, hidden Markov models (HMMs) are usually quantized and the original full space distributions are represented by combinations of a few quantized distribution prototypes. We propose a maximum likelihood objective function to train the quantized distribution prototypes. The experimental results show that the new training algorithm and the link structure adaptation scheme for the quantized HMMs reduce the word recognition error rate by 20.0%.

Speech recognition for mobile devices

This article presents an overview of different approaches for providing automatic speech recognition (ASR) technology to mobile users. Three principal system architectures with respect to the employment of a wireless communication link are analyzed: Embedded Speech Recognition Systems, Network Speech Recognition (NSR) and Distributed Speech Recognition (DSR). An overview of the solutions having been standardized so far as well as a critical analysis of the latest developments in the field of speech recognition in mobile environments is given. Open issues, pros and cons of the different methodologies and techniques are highlighted. Special emphasis is placed on the constraints and limitations ASR applications are confronted with under different architectures.

SPEECH RECOGNITION USING MULTILAYER RECURRENT NEURAL PREDICTION MODELS AND HMM

In this paper, the recurrent neural networks (RNNs) are applied to compensate for hidden Markov model (HMM) recognition algorithm, which is commonly used as a main recognizer. Among these RNNs, the multilayer recurrent neural prediction model (MRNPM), which allows operating in real-time, is used to implement learning and recognition, and HMM and MRNPM are used to design a hybrid-type main recognizer. After testing the designed speech recognition algorithm with Korean number pronunciations (13 words), which are hardly distinct, for its speech-independent recognition ratio, about a 5% improvement is obtained comparing results with existing HMM recognizers. Based on this result, only optimal (recognition) codes were extracted in the actual DSP (TMS320C6711) environment and the embedded speech recognition system was implemented. Similarly, the implementation result of the embedded system showed an improved recognition system implementation than existing solid HMM recognition systems.

Processing speech recognition errors in an embedded speech recognition system

A method for processing a misrecognition error in an embedded speech recognition system during a speech recognition session can include the step of speech-to-text converting audio input in the embedded speech recognition system based on an active language model. The speech-to-text conversion can produce speech recognized text that can be presented through a user interface. A user-initiated misrecognition error notification can be detected. The audio input and a reference to the active language model can be provided to a speech recognition system training process associated with the embedded speech recognition system.

Achieving a reliable compact acoustic model for embedded speech recognition system with high confusion frequency model handling

An acoustic model for an embedded speech recognition system must exhibit two desirable features; the ability to minimize the performance degradation in recognition, while solving the memory problem under the constraint of limited system resources. Moreover, for general speech recognition tasks, context dependent models such as state-clustered tri-phones are used to guarantee the high recognition performance of the embedded system. To cope with these challenges, we introduce the state-clustered tied-mixture (SCTM) HMM as a method of optimizing an acoustic model. The proposed SCTM modeling system offers a significant improvement in recognition performance, as well as providing a solution to sparse training data problems. Moreover, the state weight quantizing method achieves a drastic reduction in the size of the model. However, using models constructed only in this way is insufficient to improve the recognition rate in some tasks where a large mutual similarity exists, such as in the case of the Korean-digit recognition task. Hence, we also construct new dedicated HMM’s for all or part of the Korean-digits that have exclusive states using the same Gaussian pool of previous tri-phone models. In this paper, we describe the acoustic model optimization procedure for embedded speech recognition systems and the corresponding performance evaluation results.