• Title/Summary/Keyword: speech recognition

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Emotion Robust Speech Recognition using Speech Transformation (음성 변환을 사용한 감정 변화에 강인한 음성 인식)

  • Kim, Weon-Goo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.20 no.5
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    • pp.683-687
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    • 2010
  • This paper studied some methods which use frequency warping method that is the one of the speech transformation method to develope the robust speech recognition system for the emotional variation. For this purpose, the effect of emotional variations on the speech signal were studied using speech database containing various emotions and it is observed that speech spectrum is affected by the emotional variation and this effect is one of the reasons that makes the performance of the speech recognition system worse. In this paper, new training method that uses frequency warping in training process is presented to reduce the effect of emotional variation and the speech recognition system based on vocal tract length normalization method is developed to be compared with proposed system. Experimental results from the isolated word recognition using HMM showed that new training method reduced the error rate of the conventional recognition system using speech signal containing various emotions.

Proposed Efficient Architectures and Design Choices in SoPC System for Speech Recognition

  • Trang, Hoang;Hoang, Tran Van
    • Journal of IKEEE
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    • v.17 no.3
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    • pp.241-247
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    • 2013
  • This paper presents the design of a System on Programmable Chip (SoPC) based on Field Programmable Gate Array (FPGA) for speech recognition in which Mel-Frequency Cepstral Coefficients (MFCC) for speech feature extraction and Vector Quantization for recognition are used. The implementing process of the speech recognition system undergoes the following steps: feature extraction, training codebook, recognition. In the first step of feature extraction, the input voice data will be transformed into spectral components and extracted to get the main features by using MFCC algorithm. In the recognition step, the obtained spectral features from the first step will be processed and compared with the trained components. The Vector Quantization (VQ) is applied in this step. In our experiment, Altera's DE2 board with Cyclone II FPGA is used to implement the recognition system which can recognize 64 words. The execution speed of the blocks in the speech recognition system is surveyed by calculating the number of clock cycles while executing each block. The recognition accuracies are also measured in different parameters of the system. These results in execution speed and recognition accuracy could help the designer to choose the best configurations in speech recognition on SoPC.

Review And Challenges In Speech Recognition (ICCAS 2005)

  • Ahmed, M.Masroor;Ahmed, Abdul Manan Bin
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.1705-1709
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    • 2005
  • This paper covers review and challenges in the area of speech recognition by taking into account different classes of recognition mode. The recognition mode can be either speaker independent or speaker dependant. Size of the vocabulary and the input mode are two crucial factors for a speech recognizer. The input mode refers to continuous or isolated speech recognition system and the vocabulary size can be small less than hundred words or large less than few thousands words. This varies according to system design and objectives.[2]. The organization of the paper is: first it covers various fundamental methods of speech recognition, then it takes into account various deficiencies in the existing systems and finally it discloses the various probable application areas.

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Robust Speech Detection Based on Useful Bands for Continuous Digit Speech over Telephone Networks

  • Ji, Mi-Kyongi;Suh, Young-Joo;Kim, Hoi-Rin;Kim, Sang-Hun
    • The Journal of the Acoustical Society of Korea
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    • v.22 no.3E
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    • pp.113-123
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    • 2003
  • One of the most important problems in speech recognition is to detect the presence of speech in adverse environments. In other words, the accurate detection of speech boundary is critical to the performance of speech recognition. Furthermore the speech detection problem becomes severer when recognition systems are used over the telephone network, especially wireless network and noisy environment. Therefore this paper describes various speech detection algorithms for continuous digit recognition system used over wire/wireless telephone networks and we propose a algorithm in order to improve the robustness of speech detection using useful band selection under noisy telephone networks. In this paper, we compare some speech detection algorithms with the proposed one, and present experimental results done with various SNRs. The results show that the new algorithm outperforms the other speech detection methods.

Speech Parameters for the Robust Emotional Speech Recognition (감정에 강인한 음성 인식을 위한 음성 파라메터)

  • Kim, Weon-Goo
    • Journal of Institute of Control, Robotics and Systems
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    • v.16 no.12
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    • pp.1137-1142
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    • 2010
  • This paper studied the speech parameters less affected by the human emotion for the development of the robust speech recognition system. For this purpose, the effect of emotion on the speech recognition system and robust speech parameters of speech recognition system were studied using speech database containing various emotions. In this study, mel-cepstral coefficient, delta-cepstral coefficient, RASTA mel-cepstral coefficient and frequency warped mel-cepstral coefficient were used as feature parameters. And CMS (Cepstral Mean Subtraction) method were used as a signal bias removal technique. Experimental results showed that the HMM based speaker independent word recognizer using vocal tract length normalized mel-cepstral coefficient, its derivatives and CMS as a signal bias removal showed the best performance of 0.78% word error rate. This corresponds to about a 50% word error reduction as compare to the performance of baseline system using mel-cepstral coefficient, its derivatives and CMS.

Design of a Korean Speech Recognition Platform (한국어 음성인식 플랫폼의 설계)

  • Kwon Oh-Wook;Kim Hoi-Rin;Yoo Changdong;Kim Bong-Wan;Lee Yong-Ju
    • MALSORI
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    • no.51
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    • pp.151-165
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    • 2004
  • For educational and research purposes, a Korean speech recognition platform is designed. It is based on an object-oriented architecture and can be easily modified so that researchers can readily evaluate the performance of a recognition algorithm of interest. This platform will save development time for many who are interested in speech recognition. The platform includes the following modules: Noise reduction, end-point detection, met-frequency cepstral coefficient (MFCC) and perceptually linear prediction (PLP)-based feature extraction, hidden Markov model (HMM)-based acoustic modeling, n-gram language modeling, n-best search, and Korean language processing. The decoder of the platform can handle both lexical search trees for large vocabulary speech recognition and finite-state networks for small-to-medium vocabulary speech recognition. It performs word-dependent n-best search algorithm with a bigram language model in the first forward search stage and then extracts a word lattice and restores each lattice path with a trigram language model in the second stage.

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Constructing a Noise-Robust Speech Recognition System using Acoustic and Visual Information (청각 및 시가 정보를 이용한 강인한 음성 인식 시스템의 구현)

  • Lee, Jong-Seok;Park, Cheol-Hoon
    • Journal of Institute of Control, Robotics and Systems
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    • v.13 no.8
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    • pp.719-725
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    • 2007
  • In this paper, we present an audio-visual speech recognition system for noise-robust human-computer interaction. Unlike usual speech recognition systems, our system utilizes the visual signal containing speakers' lip movements along with the acoustic signal to obtain robust speech recognition performance against environmental noise. The procedures of acoustic speech processing, visual speech processing, and audio-visual integration are described in detail. Experimental results demonstrate the constructed system significantly enhances the recognition performance in noisy circumstances compared to acoustic-only recognition by using the complementary nature of the two signals.

Development of a Weather Forecast Service Based on AIN Using Speech Recognition (음성 인식을 이용한 지능망 기반 일기예보 서비스 개발)

  • Park Sung-Joon;Kim Jae-In;Koo Myoung-Wan;Jhon Chu-Shik
    • MALSORI
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    • no.51
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    • pp.137-149
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    • 2004
  • A weather forecast service with speech recognition is described. This service allows users to get the weather information of all the cities by saying the city names with just one phone call, which was not provided in the previous weather forecast service. Speech recognition is implemented in the intelligent peripheral (IP) of the advanced intelligent network (AIN). The AIN is a telephone network architecture that separates service logic from switching equipment, allowing new services to be added without having to redesign switches to support new services. Experiments in speech recognition show that the recognition accuracy is 90.06% for the general users' speech database. For the laboratory members' speech database, the accuracies are 95.04% and 93.81%, respectively in simulation and in the test on the developed system.

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Development of a Work Management System Based on Speech and Speaker Recognition

  • Gaybulayev, Abdulaziz;Yunusov, Jahongir;Kim, Tae-Hyong
    • IEMEK Journal of Embedded Systems and Applications
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    • v.16 no.3
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    • pp.89-97
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    • 2021
  • Voice interface can not only make daily life more convenient through artificial intelligence speakers but also improve the working environment of the factory. This paper presents a voice-assisted work management system that supports both speech and speaker recognition. This system is able to provide machine control and authorized worker authentication by voice at the same time. We applied two speech recognition methods, Google's Speech application programming interface (API) service, and DeepSpeech speech-to-text engine. For worker identification, the SincNet architecture for speaker recognition was adopted. We implemented a prototype of the work management system that provides voice control with 26 commands and identifies 100 workers by voice. Worker identification using our model was almost perfect, and the command recognition accuracy was 97.0% in Google API after post- processing and 92.0% in our DeepSpeech model.

A Real-Time Implementation of Speech Recognition System Using Oak DSP core in the Car Noise Environment (자동차 환경에서 Oak DSP 코어 기반 음성 인식 시스템 실시간 구현)

  • Woo, K.H.;Yang, T.Y.;Lee, C.;Youn, D.H.;Cha, I.H.
    • Speech Sciences
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    • v.6
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    • pp.219-233
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    • 1999
  • This paper presents a real-time implementation of a speaker independent speech recognition system based on a discrete hidden markov model(DHMM). This system is developed for a car navigation system to design on-chip VLSI system of speech recognition which is used by fixed point Oak DSP core of DSP GROUP LTD. We analyze recognition procedure with C language to implement fixed point real-time algorithms. Based on the analyses, we improve the algorithms which are possible to operate in real-time, and can verify the recognition result at the same time as speech ends, by processing all recognition routines within a frame. A car noise is the colored noise concentrated heavily on the low frequency segment under 400 Hz. For the noise robust processing, the high pass filtering and the liftering on the distance measure of feature vectors are applied to the recognition system. Recognition experiments on the twelve isolated command words were performed. The recognition rates of the baseline recognizer were 98.68% in a stopping situation and 80.7% in a running situation. Using the noise processing methods, the recognition rates were enhanced to 89.04% in a running situation.

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